02 - Regression.ipynb

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    "# Regression\n",
    "\n",
    "*Supervised* machine learning techniques involve training a model to operate on a set of *features* and predict a *label* using a dataset that includes some already-known label values. The training process *fits* the features to the known labels to define a general function that can be applied to new features for which the labels are unknown, and predict them. You can think of this function like this, in which ***y*** represents the label we want to predict and ***x*** represents the features the model uses to predict it.\n",
    "\n",
    "$$y = f(x)$$\n",
    "\n",
    "In most cases, *x* is actually a *vector* that consists of multiple feature values, so to be a little more precise, the function could be expressed like this:\n",
    "\n",
    "$$y = f([x_1, x_2, x_3, ...])$$\n",
    "\n",
    "The goal of training the model is to find a function that performs some kind of calculation to the *x* values that produces the result *y*. We do this by applying a machine learning *algorithm* that tries to fit the *x* values to a calculation that produces *y* reasonably accurately for all of the cases in the training dataset.\n",
    "\n",
    "There are lots of machine learning algorithms for supervised learning, and they can be broadly divided into two types:\n",
    "\n",
    "- **_Regression_ algorithms**: Algorithms that predict a *y* value that is a numeric value, such as the price of a house or the number of sales transactions.\n",
    "- **_Classification_ algorithms**: Algorithms that predict to which category, or *class*, an observation belongs. The *y* value in a classification model is a vector of probability values between 0 and 1, one for each class, indicating the probability of the observation belonging to each class.\n",
    "\n",
    "In this notebook, we'll focus on *regression*, using an example based on a real study in which data for a bicycle sharing scheme was collected and used to predict the number of rentals based on seasonality and weather conditions. We'll use a simplified version of the dataset from that study.\n",
    "\n",
    "> **Citation**: The data used in this exercise is derived from [Capital Bikeshare](https://www.capitalbikeshare.com/system-data) and is used in accordance with the published [license agreement](https://www.capitalbikeshare.com/data-license-agreement).\n",
    "\n",
    "## Explore the Data\n",
    "\n",
    "The first step in any machine learning project is to explore the data that you will use to train a model. The goal of this exploration is to try to understand the relationships between its attributes; in particular, any apparent correlation between the *features* and the *label* your model will try to predict. This may require some work to detect and fix issues in the data (such as dealing with missing values, errors, or outlier values), deriving new feature columns by transforming or combining existing features (a process known as *feature engineering*), *normalizing* numeric features (values you can measure or count) so they're on a similar scale, and *encoding* categorical features (values that represent discrete categories) as numeric indicators.\n",
    "\n",
    "Let's start by loading the bicycle sharing data as a **Pandas** DataFrame and viewing the first few rows."
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>rentals</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
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       "   instant    dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  1/1/2011       1   0     1        0        6           0   \n",
       "1        2  1/2/2011       1   0     1        0        0           0   \n",
       "2        3  1/3/2011       1   0     1        0        1           1   \n",
       "3        4  1/4/2011       1   0     1        0        2           1   \n",
       "4        5  1/5/2011       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  rentals  \n",
       "0           2  0.344167  0.363625  0.805833   0.160446      331  \n",
       "1           2  0.363478  0.353739  0.696087   0.248539      131  \n",
       "2           1  0.196364  0.189405  0.437273   0.248309      120  \n",
       "3           1  0.200000  0.212122  0.590435   0.160296      108  \n",
       "4           1  0.226957  0.229270  0.436957   0.186900       82  "
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   "source": [
    "import pandas as pd\n",
    "\n",
    "# load the training dataset\n",
    "bike_data = pd.read_csv('data/daily-bike-share.csv')\n",
    "bike_data.head()"
   ]
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    "The data consists of the following columns:\n",
    "\n",
    "- **instant**: A unique row identifier\n",
    "- **dteday**: The date on which the data was observed - in this case, the data was collected daily; so there's one row per date.\n",
    "- **season**: A numerically encoded value indicating the season (1:spring, 2:summer, 3:fall, 4:winter)\n",
    "- **yr**: The year of the study in which the observation was made (the study took place over two years - year 0 represents 2011, and year 1 represents 2012)\n",
    "- **mnth**: The calendar month in which the observation was made (1:January ... 12:December)\n",
    "- **holiday**: A binary value indicating whether or not the observation was made on a public holiday)\n",
    "- **weekday**: The day of the week on which the observation was made (0:Sunday ... 6:Saturday)\n",
    "- **workingday**: A binary value indicating whether or not the day is a working day (not a weekend or holiday)\n",
    "- **weathersit**: A categorical value indicating the weather situation (1:clear, 2:mist/cloud, 3:light rain/snow, 4:heavy rain/hail/snow/fog)\n",
    "- **temp**: The temperature in celsius (normalized)\n",
    "- **atemp**: The apparent (\"feels-like\") temperature in celsius (normalized)\n",
    "- **hum**: The humidity level (normalized)\n",
    "- **windspeed**: The windspeed (normalized)\n",
    "- **rentals**: The number of bicycle rentals recorded.\n",
    "\n",
    "In this dataset, **rentals** represents the label (the *y* value) our model must be trained to predict. The other columns are potential features (*x* values).\n",
    "\n",
    "As mentioned previously, you can perform some *feature engineering* to combine or derive new features. For example, let's add a new column named **day** to the dataframe by extracting the day component from the existing **dteday** column. The new column represents the day of the month from 1 to 31."
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       "      <td>0</td>\n",
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       "      <td>1/7/2011</td>\n",
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       "      <td>0</td>\n",
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       "      <td>8</td>\n",
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       "      <td>1/9/2011</td>\n",
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       "      <td>0.361950</td>\n",
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       "      <td>9</td>\n",
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       "      <td>10</td>\n",
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       "      <td>1/11/2011</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.191464</td>\n",
       "      <td>0.686364</td>\n",
       "      <td>0.122132</td>\n",
       "      <td>43</td>\n",
       "      <td>11</td>\n",
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       "      <td>13</td>\n",
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       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1/14/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.188413</td>\n",
       "      <td>0.537826</td>\n",
       "      <td>0.126548</td>\n",
       "      <td>54</td>\n",
       "      <td>14</td>\n",
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       "      <td>15</td>\n",
       "      <td>1/15/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.248112</td>\n",
       "      <td>0.498750</td>\n",
       "      <td>0.157963</td>\n",
       "      <td>222</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1/16/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.231667</td>\n",
       "      <td>0.234217</td>\n",
       "      <td>0.483750</td>\n",
       "      <td>0.188433</td>\n",
       "      <td>251</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>1/17/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.175833</td>\n",
       "      <td>0.176771</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>0.194017</td>\n",
       "      <td>117</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1/18/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>0.232333</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.146775</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>1/19/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.292174</td>\n",
       "      <td>0.298422</td>\n",
       "      <td>0.741739</td>\n",
       "      <td>0.208317</td>\n",
       "      <td>78</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1/20/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.261667</td>\n",
       "      <td>0.255050</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.195904</td>\n",
       "      <td>83</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>1/21/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.177500</td>\n",
       "      <td>0.157833</td>\n",
       "      <td>0.457083</td>\n",
       "      <td>0.353242</td>\n",
       "      <td>75</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1/22/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.171970</td>\n",
       "      <td>93</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1/23/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.096522</td>\n",
       "      <td>0.098839</td>\n",
       "      <td>0.436522</td>\n",
       "      <td>0.246600</td>\n",
       "      <td>150</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1/24/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.097391</td>\n",
       "      <td>0.117930</td>\n",
       "      <td>0.491739</td>\n",
       "      <td>0.158330</td>\n",
       "      <td>86</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1/25/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.223478</td>\n",
       "      <td>0.234526</td>\n",
       "      <td>0.616957</td>\n",
       "      <td>0.129796</td>\n",
       "      <td>186</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1/26/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.217500</td>\n",
       "      <td>0.203600</td>\n",
       "      <td>0.862500</td>\n",
       "      <td>0.293850</td>\n",
       "      <td>34</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>1/27/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.195000</td>\n",
       "      <td>0.219700</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.113837</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1/28/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.203478</td>\n",
       "      <td>0.223317</td>\n",
       "      <td>0.793043</td>\n",
       "      <td>0.123300</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1/29/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196522</td>\n",
       "      <td>0.212126</td>\n",
       "      <td>0.651739</td>\n",
       "      <td>0.145365</td>\n",
       "      <td>123</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>1/30/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.216522</td>\n",
       "      <td>0.250322</td>\n",
       "      <td>0.722174</td>\n",
       "      <td>0.073983</td>\n",
       "      <td>140</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>1/31/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.180833</td>\n",
       "      <td>0.186250</td>\n",
       "      <td>0.603750</td>\n",
       "      <td>0.187192</td>\n",
       "      <td>42</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>2/1/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.192174</td>\n",
       "      <td>0.234530</td>\n",
       "      <td>0.829565</td>\n",
       "      <td>0.053213</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    instant     dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0         1   1/1/2011       1   0     1        0        6           0   \n",
       "1         2   1/2/2011       1   0     1        0        0           0   \n",
       "2         3   1/3/2011       1   0     1        0        1           1   \n",
       "3         4   1/4/2011       1   0     1        0        2           1   \n",
       "4         5   1/5/2011       1   0     1        0        3           1   \n",
       "5         6   1/6/2011       1   0     1        0        4           1   \n",
       "6         7   1/7/2011       1   0     1        0        5           1   \n",
       "7         8   1/8/2011       1   0     1        0        6           0   \n",
       "8         9   1/9/2011       1   0     1        0        0           0   \n",
       "9        10  1/10/2011       1   0     1        0        1           1   \n",
       "10       11  1/11/2011       1   0     1        0        2           1   \n",
       "11       12  1/12/2011       1   0     1        0        3           1   \n",
       "12       13  1/13/2011       1   0     1        0        4           1   \n",
       "13       14  1/14/2011       1   0     1        0        5           1   \n",
       "14       15  1/15/2011       1   0     1        0        6           0   \n",
       "15       16  1/16/2011       1   0     1        0        0           0   \n",
       "16       17  1/17/2011       1   0     1        1        1           0   \n",
       "17       18  1/18/2011       1   0     1        0        2           1   \n",
       "18       19  1/19/2011       1   0     1        0        3           1   \n",
       "19       20  1/20/2011       1   0     1        0        4           1   \n",
       "20       21  1/21/2011       1   0     1        0        5           1   \n",
       "21       22  1/22/2011       1   0     1        0        6           0   \n",
       "22       23  1/23/2011       1   0     1        0        0           0   \n",
       "23       24  1/24/2011       1   0     1        0        1           1   \n",
       "24       25  1/25/2011       1   0     1        0        2           1   \n",
       "25       26  1/26/2011       1   0     1        0        3           1   \n",
       "26       27  1/27/2011       1   0     1        0        4           1   \n",
       "27       28  1/28/2011       1   0     1        0        5           1   \n",
       "28       29  1/29/2011       1   0     1        0        6           0   \n",
       "29       30  1/30/2011       1   0     1        0        0           0   \n",
       "30       31  1/31/2011       1   0     1        0        1           1   \n",
       "31       32   2/1/2011       1   0     2        0        2           1   \n",
       "\n",
       "    weathersit      temp     atemp       hum  windspeed  rentals  day  \n",
       "0            2  0.344167  0.363625  0.805833   0.160446      331    1  \n",
       "1            2  0.363478  0.353739  0.696087   0.248539      131    2  \n",
       "2            1  0.196364  0.189405  0.437273   0.248309      120    3  \n",
       "3            1  0.200000  0.212122  0.590435   0.160296      108    4  \n",
       "4            1  0.226957  0.229270  0.436957   0.186900       82    5  \n",
       "5            1  0.204348  0.233209  0.518261   0.089565       88    6  \n",
       "6            2  0.196522  0.208839  0.498696   0.168726      148    7  \n",
       "7            2  0.165000  0.162254  0.535833   0.266804       68    8  \n",
       "8            1  0.138333  0.116175  0.434167   0.361950       54    9  \n",
       "9            1  0.150833  0.150888  0.482917   0.223267       41   10  \n",
       "10           2  0.169091  0.191464  0.686364   0.122132       43   11  \n",
       "11           1  0.172727  0.160473  0.599545   0.304627       25   12  \n",
       "12           1  0.165000  0.150883  0.470417   0.301000       38   13  \n",
       "13           1  0.160870  0.188413  0.537826   0.126548       54   14  \n",
       "14           2  0.233333  0.248112  0.498750   0.157963      222   15  \n",
       "15           1  0.231667  0.234217  0.483750   0.188433      251   16  \n",
       "16           2  0.175833  0.176771  0.537500   0.194017      117   17  \n",
       "17           2  0.216667  0.232333  0.861667   0.146775        9   18  \n",
       "18           2  0.292174  0.298422  0.741739   0.208317       78   19  \n",
       "19           2  0.261667  0.255050  0.538333   0.195904       83   20  \n",
       "20           1  0.177500  0.157833  0.457083   0.353242       75   21  \n",
       "21           1  0.059130  0.079070  0.400000   0.171970       93   22  \n",
       "22           1  0.096522  0.098839  0.436522   0.246600      150   23  \n",
       "23           1  0.097391  0.117930  0.491739   0.158330       86   24  \n",
       "24           2  0.223478  0.234526  0.616957   0.129796      186   25  \n",
       "25           3  0.217500  0.203600  0.862500   0.293850       34   26  \n",
       "26           1  0.195000  0.219700  0.687500   0.113837       15   27  \n",
       "27           2  0.203478  0.223317  0.793043   0.123300       38   28  \n",
       "28           1  0.196522  0.212126  0.651739   0.145365      123   29  \n",
       "29           1  0.216522  0.250322  0.722174   0.073983      140   30  \n",
       "30           2  0.180833  0.186250  0.603750   0.187192       42   31  \n",
       "31           2  0.192174  0.234530  0.829565   0.053213       47    1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data['day'] = pd.DatetimeIndex(bike_data['dteday']).day\n",
    "bike_data.head(32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, let's start our analysis of the data by examining a few key descriptive statistics. We can use the dataframe's **describe** method to generate these for the numeric features as well as the **rentals** label column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "gather": {
     "logged": 1667468531563
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>rentals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             temp       atemp         hum   windspeed      rentals\n",
       "count  731.000000  731.000000  731.000000  731.000000   731.000000\n",
       "mean     0.495385    0.474354    0.627894    0.190486   848.176471\n",
       "std      0.183051    0.162961    0.142429    0.077498   686.622488\n",
       "min      0.059130    0.079070    0.000000    0.022392     2.000000\n",
       "25%      0.337083    0.337842    0.520000    0.134950   315.500000\n",
       "50%      0.498333    0.486733    0.626667    0.180975   713.000000\n",
       "75%      0.655417    0.608602    0.730209    0.233214  1096.000000\n",
       "max      0.861667    0.840896    0.972500    0.507463  3410.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features = ['temp', 'atemp', 'hum', 'windspeed']\n",
    "bike_data[numeric_features + ['rentals']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The statistics reveal some information about the distribution of the data in each of the numeric fields, including the number of observations (there are 731 records), the mean, standard deviation, minimum and maximum values, and the quartile values (the threshold values for 25%, 50% - which is also the median, and 75% of the data). From this, we can see that the mean number of daily rentals is around 848; but there's a comparatively large standard deviation, indicating a lot of variance in the number of rentals per day.\n",
    "\n",
    "We might get a clearer idea of the distribution of rentals values by visualizing the data. Common plot types for visualizing numeric data distributions are *histograms* and *box plots*, so let's use Python's **matplotlib** library to create one of each of these for the **rentals** column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "gather": {
     "logged": 1667468532890
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This ensures plots are displayed inline in the Jupyter notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Get the label column\n",
    "label = bike_data['rentals']\n",
    "\n",
    "\n",
    "# Create a figure for 2 subplots (2 rows, 1 column)\n",
    "fig, ax = plt.subplots(2, 1, figsize = (9,12))\n",
    "\n",
    "# Plot the histogram   \n",
    "ax[0].hist(label, bins=100)\n",
    "ax[0].set_ylabel('Frequency')\n",
    "\n",
    "# Add lines for the mean, median, and mode\n",
    "ax[0].axvline(label.mean(), color='magenta', linestyle='dashed', linewidth=2)\n",
    "ax[0].axvline(label.median(), color='cyan', linestyle='dashed', linewidth=2)\n",
    "\n",
    "# Plot the boxplot   \n",
    "ax[1].boxplot(label, vert=False)\n",
    "ax[1].set_xlabel('Rentals')\n",
    "\n",
    "# Add a title to the Figure\n",
    "fig.suptitle('Rental Distribution')\n",
    "\n",
    "# Show the figure\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots show that the number of daily rentals ranges from 0 to just over 3,400. However, the mean (and median) number of daily rentals is closer to the low end of that range, with most of the data between 0 and around 2,200 rentals. The few values above this are shown in the box plot as small circles, indicating that they are *outliers* - in other words, unusually high or low values beyond the typical range of most of the data.\n",
    "\n",
    "We can do the same kind of visual exploration of the numeric features. Let's create a histogram for each of these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "gather": {
     "logged": 1667468535956
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhgAAAF1CAYAAACqBpMJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAc/klEQVR4nO3dfZRUd33H8c9XY3I0GzAPuiBZWB9SbWQTKivoEezu8QndECQ+HCHxsPUBbYXYY06bh3parUcCtbGtxB7FqtBKslYNJ4FYYppmk+CpSSFClhhtYrqEIEsMRGB9QNFv/5i7ZrLcufcy85u5O3Pfr3PmzM7c75357Y/ZySe/3733Z+4uAACAkJ6VdwMAAEDrIWAAAIDgCBgAACA4AgYAAAiOgAEAAIIjYAAAgOAIGECBmdmomb2kyn0HzewDodtULTPrN7NtebcDQMkpeTcAQH7cvS3vNgBoTYxgAACA4AgYQAsysz8xs81ljx82s2+UPd5rZrPMzM3sZdFz683s82Z2q5kdNbN7zeylZfu8ycx+aGaHzex6SVa27WVmdle07Ukz+3rZNjezy83s0WjbZ8zsWWXb32dmD5nZU2Z2m5nNKNv2CjO73cwOmdmPzOzdZdvONrNbzOyImd0n6fdtBZA/AgbQmu6SNN/MnmVmL5J0qqTXSlJ0zEWbpAdi9nuPpE9KOlPSI5I+He1zjqSbJH1c0jmSfizpdWX7fUrSd6L9zpW0dtzrLpbULelVkhZJel/0uoskXSPpEkkvkHSPpBujbadLul3SDZJeGLXtn83s/Og1Py/pV5KmRq/3vuzdA6DeCBhAC3L3RyUdlTRL0usl3SbpJ2b2Ckl/LOked/9dzK6b3P0+dz8uaWO0vyS9TdKD7v5Nd/+NpH+UNFK2328kzZD0Inf/lbuPP9hyjbsfcvfHon2XRM9/WNK17v5Q9J6rJM2KRjEukjTs7l919+Pu/n1J35L0LjN7tqR3SPprd/+5u++WtKHK7gJQBwQMoHXdJalHpYBxl6RBlcLFH0eP45SHhl+oNNIhSS+StHdsg5dWSdxbVvuXKk2Z3GdmD5rZ+NGE8to90etJpVDyT2b2MzP7maRD0etMi7bNHdsWbb9U0hSVRjtOiXldABMEZ5EAresuSQslvVilkYGx/0C/VtL1J/la+yV1jD0wMyt/7O4jkj4YbZsn6T/N7G53fyQq6ZD0YPTzdEk/iX7eK+nT7r5x/BtGoxh3ufubYrY9W9Lx6HV/WPa6ACYIRjCA1nWXpF5Jz3X3x1U6vmGBpLMlff8kX+tWSa80s0vM7BRJl6s0kiBJMrN3mdm50cOnJLmk8imYvzCzM82sQ9JHJY0dBPoFSVeb2Suj15lsZu+Ktm2R9Adm9l4ze050e7WZ/aG7/1alY0I+YWbPi47LWHaSvxOAOiJgAC3K3f9X0qhKwULufkTSo5K+G/0H+mRe60lJ75K0WtJBSedJ+m5Zyasl3Wtmo5JukfTR6DiQMTdL2iFpp0ph5cvR626StEbSgJkdkbRb0lujbUclvVmlgzt/otL0zRpJp0WvuUKlKZwRSeslffVkficA9WWlqVQAqA8zc0nnlU2XACgARjAAAEBwBAwAABAcUyQAACA4RjAAAEBwqQHDzDrM7E4z+0F0AZ2PRs9/wsz2mdnO6Pa2+jcXAAA0g9QpEjObKmmqu99vZmeodKrZ2yW9W9Kou/991jc755xzvLOzUz//+c91+umn19JuVIF+z+6n0f0Lai6i3/NCv+eDfs9HXv2+Y8eOJ9099lsw9Uqe7r5fpav4yd2PmtlDKl3G96R1dnZq+/btGhwcVE9PTzUvgRrQ79mNLROaeO3pTEX0e17o93zQ7/nIq9/NrOI34Ekd5GlmnZLuljRT0sck9Us6Imm7pCvc/amYfZZLWi5J7e3tswcGBjQ6Oqq2trbxpagz+j273ugP9c7BwYo1Pb2lmsE7K9dI9Hte6Pd80O/5yKvfe3t7d7h7d9y2zAHDzNpUuvTwp939JjNrl/SkSpcE/pRK0yiJyyV3d3c7Ixj5od+zGxucSPzryFREv+eFfs8H/Z6PHEcwKgaMTGeRmNlzVFomeaO73yRJ7n7A3X8bLfn8JUlzQjUYAAA0tyxnkZhK6wY85O6fLXt+alnZYpXWEAAAAMi0XPvrJL1X0pCZ7Yyeu0bSEjObpdIA8bCkD9WlhQAAoOlkOYtkm56ebS737fDNAQAArSDLCAZQOJkOfeYq+wBQEZcKBwAAwREwAABAcAQMIMbs6FZ7EQAUE8dgADHuD1YEAMXECAYAAAiOgAEAAIJjigQtqfOqW094bnh1Xw4tAYBiYgQDAAAER8AAAADBMUUCxPhgsCIAKCYCBhBjXbAiACgmpkgAAEBwBAwgxo7oVnsRABQTUyRAjO7oPnHB1ExFAFBMjGAAAIDgCBgAACA4AgYAAAiOgAEAAIIjYAAAgOA4iwQNM34BMhYfA4DWRcAAYmwPVgQAxUTAAGLMDlYEAMXEMRgAACA4AgYQY3l0q70IAIqJgAHE+FJ0q70IAIqJgAEAAIIjYAAAgOAIGAAAIDgCBgAACI6AAQAAguNCW0CMVwUrAoBiImAAMXYEKwKAYiJgAAnGL9Am5b9I20RsEwCMxzEYAAAgOAIGEMOiW+1FAFBMBAwAABAcAQMAAARHwAAAAMERMAAAQHAEDAAAEBwBAwAABMeFtoAYX4zuV2UpAgCcgIABxFge3ScGjOVJGwGg2JgiAQAAwTGCAcRYdzJFjGQAwAkIGKiLuAW5msmHovsZMdvGfrfhNaUFxjofvZXFxgBgHKZIAABAcAQMAAAQHAEDAAAER8AAAADBETAAAEBwBAwAABAcp6kCMTy670yo6byyuU/FBYB6YgQDAAAER8AAAADBpQYMM+swszvN7Adm9qCZfTR6/iwzu93MHo7uz6x/c4HGmB3dkmxeP0+b189rRHMAoOlkGcE4LukKdz9f0mskfcTMzpd0laQ73P08SXdEj4GWcH90S9J1YLK6DkxuRHMAoOmkBgx33+/u90c/H5X0kKRpkhZJ2hCVbZD09no1EgAANBdz9/SqsWKzTkl3S5op6TF3f370vEl6auzxuH2WK1pvsr29ffbAwIBGR0fV1tZWe+txUhrZ70P7DqfWdE078f/+x++XpSaurtaa3p4eSdLnNt58Qs2YlZctkiSt/drNse0cE7rfs/xuaOznHU+j3/ORV7/39vbucPfuuG2ZT1M1szZJ35L05+5+pJQpStzdzSw2qbj7OkULW3d3d3tPT48GBwfVE32Bo3Ea2e/9GVZTHb60J3W/LDVxdaFqrhuq/Ceysqwmrp1jQvd7lnajsZ93PI1+z8dE7PdMZ5GY2XNUChcb3f2m6OkDZjY12j5V0hP1aSIAAGg2Wc4iMUlflvSQu3+2bNMtkpZFPy+TVHksGQAAFEqWKZLXSXqvpCEz2xk9d42k1ZL+3czeL2mPpHfXp4lA430wuv9OQs0NFz7WiKYAQFNKDRjuvk2SVdj8hrDNASaGddF9Z0LNNQuGGtASAGhOXMkTAAAEx2JnQIwdGWpmjkySJO2ecqS+jQGAJkTAAGKMndQ9I6Fmy4b5klhVFQDiMEUCAACCI2AAAIDgCBgAACA4AgYAAAiOgzyRqnP8+iCr+3JqSW3G/x55i2tPtX3bKv9GAFoHIxgAACA4RjCAGNuj+3ck1Fy07J5GNAUAmhIBA4gxO0MNF9gCgMqYIgEAAMExggHEWJ6hZtXWLkksegYAcRjBAGJ8KbolWbprupbumt6I5gBA0yFgAACA4AgYAAAgOAIGAAAIjoABAACCI2AAAIDgOE0ViPGq6P5gQs1Q++FGNAUAmhIBA0FUs5BYln3yWqBsx9j7J9Qs7N/WgJYAQHNiigQAAARHwAAAAMERMIAYFt2SDK/p0/CavkY0BwCaDgEDAAAER8AAAADBETAAAEBwBAwAABAcAQMAAARHwAAAAMFxJU8gxhej+1UJNVe/5YFGNAUAmhIBA4ixPLpPChg3ztrbiKYAQFNiigQAAATHCAYQY12GmiU7OyQ1z0jG+IXjhldzFVIA9UPAAGJ8KLqfkVBz7W0XSGqegAEAjcQUCQAACI6AAQAAgiNgAACA4AgYAAAgOAIGAAAIjoABAACC4zRVIIZH950JNZ1X3pqwFQCKjREMAAAQHAEDAAAExxQJEGN2hprN6+dJkhb2b6tvYwCgCREwgBj3R/dJlwrvOjC5EU0BgKZEwADK/H5BsCZfCGz8wmYA0GgcgwEAAIIjYAAAgOAIGAAAIDgCBgAACI6DPIEYbfc+llpzw4XpNQBQVAQMIMbZm4ZSa65ZkF4DAEXFFAkAAAiOgAHEODZtko5Nm5RYM3NkkmaOJNcAQFGlBgwz+4qZPWFmu8ue+4SZ7TOzndHtbfVtJtBYIyvna2Tl/MSaLRvma8uG5BoAKKosIxjrJS2Ief4f3H1WdPt22GYBAIBmlhow3P1uSYca0BYAANAiajkGY4WZPRBNoZwZrEUAAKDpmbunF5l1Stri7jOjx+2SnpTkkj4laaq7v6/CvsslLZek9vb22QMDAxodHVVbW1uQXwDZVdvvQ/sO16E1YXVNe+bKprW2+fJLF0mSPrfx5oo1Ky8r1az92s0nvH+5Sv2epY1xrxvq3yOpza2A75l80O/5yKvfe3t7d7h7d9y2qq6D4e4Hxn42sy9J2pJQu07SOknq7u72np4eDQ4Oqqenp5q3Rg2q7ff+JliZc/jSnmc8DtXm64Yq/4msLKsZ//7lKvV7ljbGvW6o3y2pza2A75l80O/5mIj9XtUUiZlNLXu4WNLuSrUAAKB4UkcwzOxGST2SzjGzxyX9jaQeM5ul0hTJsKQP1bGNQMNNWXtPas1Fy9JrAKCoUgOGuy+JefrLdWgLMGGctu9Ias3uKek1AFBUXMkTAAAER8AAYhxc3KWDi7sSa1Zt7dKqrck1AFBUBAwgxujc6RqdOz2xZumu6Vq6K7kGAIqKgAEAAIIjYAAAgOAIGAAAIDgCBgAACI6AAQAAgqtqLRI0Vue4tSeGV/c17L2aReh2n/p4+oJiQ+1P1yS9/xVdx9V/1a11/XcDgImGgAHEmHr9ttSahf3pNQBQVEyRAACA4AgYAAAgOAIGEGPP6j7tSTlmYnhNn4bXcFwFAMQhYAAAgOAIGAAAIDgCBgAACI6AAQAAgiNgAACA4AgYAAAgOK7kCcQ466YHUmuufkt6DQAUFQEDiHHGfXtTa26clV4DAEVFwAAapFkXkgOAanAMBhDj6JwOHZ3TkVizZGeHluxMrgGAomIEA4hx6JILJCVPlVx7W6mGqRIAOBEjGAAAIDgCBgAACI6AAQAAgiNgAACA4AgYAAAgOAIGAAAIjtNUgRgzMlwUq/NKLpwFAJUwggEAAIIjYAAAgOCYIimQoX2H1V829D+8ui/H1kxs+1fMkyRNvX5bxZrN60s1C/sr1wBAUREwgBi/Pndyak3XgfQaACgqpkgAAEBwBAwAABAcAQMAAARHwAAAAMERMAAAQHCcRQLEaLv3sdSaGy5MrwGAoiJgADHO3jSUWnPNgvQaACgqpkgAAEBwBAwgxrFpk3Rs2qTEmpkjkzRzJLkGAIqKgAHEGFk5XyMr5yfWbNkwX1s2JNcAQFERMAAAQHAc5Amgos6yxfHGsEgegCwYwQAAAMERMAAAQHAEDAAAEBwBAwAABMdBnkCMKWvvSa25aFl6DQAUFQEDiHHaviOpNbunpNcAQFExRQIAAIIjYAAxDi7u0sHFXYk1q7Z2adXW5BoAKCoCBhBjdO50jc6dnlizdNd0Ld2VXAMARZUaMMzsK2b2hJntLnvuLDO73cweju7PrG8zAQBAM8kygrFe0oJxz10l6Q53P0/SHdFjAAAASRkChrvfLenQuKcXSdoQ/bxB0tsDtwsAADQxc/f0IrNOSVvcfWb0+Gfu/vzoZ5P01NjjmH2XS1ouSe3t7bMHBgY0Ojqqtra2ML9BAQztO/yMx13TJldV88Shwzrwy5N7naK6/NJFkqTPbby5Ys3Ky0o1a79WuUaS2p+rZ/T7RBH37z9e3Ochy371lOWzLonvmZzQ7/nIq997e3t3uHt33Laar4Ph7m5mFVOKu6+TtE6Suru7vaenR4ODg+rp6an1rQujf9yKlsOX9lRVs3bjzbpu6JTEmvGvU3Tl/TXeygw1knRF1/HUmjzE/fuPF/d5yLJfPWX5rEvieyYn9Hs+JmK/V/utd8DMprr7fjObKumJkI0C8nbq4+kjOUPtjPYAQCXVBoxbJC2TtDq6Tx4jBprM1Ou3pdYs7E+vAYCiynKa6o2S/lvSy83scTN7v0rB4k1m9rCkN0aPAQAAJGUYwXD3JRU2vSFwWwAAQIvgSp5AjD2r+7RndV9izfCaPg2vSa4BgKIiYAAAgOAIGAAAIDgCBgAACI6AAQAAgiNgAACA4AgYAAAguIm3QAIwAZx10wOpNVe/Jb0GAIqKgAHEOOO+vak1N85KrwGAomKKBAAABEfAAGIcndOho3M6EmuW7OzQkp3JNQBQVEyRADEOXXKBpOSpkmtvK9UwVQIAJ2IEAwAABEfAAAAAwREwAABAcAQMAAAQHAEDAAAER8AAAADBcZoqEGPGVbem1nRemV4DAEXFCAYAAAiOEQygoDozjNJU+zrDq/tS6+Jq6inv9x8va78BzYoRDCDG/hXztH/FvMSazevnafP65BoAKCpGMIAYvz53cmpN14H0GgAoKkYwAABAcAQMAAAQHAEDAAAER8AAAADBETAAAEBwnEUCxGi797HUmhsuTK8BgKIiYAAxzt40lFpzzYL0GgAoKqZIAABAcAQMIMaxaZN0bNqkxJqZI5M0cyS5BgCKioABxBhZOV8jK+cn1mzZMF9bNiTXAEBRcQxGneS9kFHc+1/RlV4DhMBnqzoTbUE2oBaMYAAAgOAIGAAAIDgCBgAACI6AAQAAgiNgAACA4DiLBIgxZe09qTUXLUuvAYCiImAAMU7bdyS1ZveU9BoAKCqmSAAAQHAEDCDGwcVdOri4K7Fm1dYurdqaXAMARUXAAGKMzp2u0bnTE2uW7pqupbuSawCgqAgYAAAgOAIGAAAIjrNIcpT3gmhAnrIsiJbl74G/I2BiYgQDAAAER8AAAADBMUUCxDj18cOpNUPt6TUAUFQEDCDG1Ou3pdYs7E+vAYCiYooEAAAER8AAAADBETCAGHtW92lPyqmOw2v6NLyG0yEBIA4BAwAABEfAAAAAwdV0FomZDUs6Kum3ko67e3eIRgEAgOYW4jTVXnd/MsDrAACAFsEUCQAACM7cvfqdzf5P0lOSXNIX3X1dTM1yScslqb29ffbAwIBGR0fV1tZW9ftOREP7Jv5VHdufKx34Zd6taA6XX7pIkvS5jTdXrFl5Walm7dcq10j0ey26pk0+4bmsf2tp/R732o2U5ffIu43VaMXv92aQV7/39vbuqHR4RK0BY5q77zOzF0q6XdJKd7+7Un13d7dv375dg4OD6unpqfp9J6IsK0Pm7Yqu47puiIu3ZnF0Tock6Yz79lasWbKzVHPjrMo1Ev1ei7hVUbP+raX1e94rroZaTXaiacXv92aQV7+bWcWAUdO3nrvvi+6fMLNNkuZIqhgwgGaRFCzGpAULACiyqo/BMLPTzeyMsZ8lvVnS7lANAwAAzauWEYx2SZvMbOx1bnD3rUFaBeQs5BQJABRR1QHD3R+VdGHAtgATxqFLLpCUHDCuva1UQ8AAgBNxmioAAAiOgAEAAIIjYAAAgOAIGAAAIDgCBgAACI6AAQAAguP6xUCMGRku49x55cS/PDwA5IWAAWDCmohr/IxvUzOuFwI0AlMkAAAgOAIGEGP/innav2JeYs3m9fO0eX1yDQAUFVMkQIxfnzs5tabrQHoNABQVIxgAACA4AgYAAAiOgAEAAIIjYAAAgOAIGAAAIDjOIgFitN37WGrNDRem1wBAUREwgBhnbxpKrblmQXoNABQVUyQAACA4AgYQ49i0STo2bVJizcyRSZo5klwDAEXFFMk4WRZXYnGj1jeycr6k5FVVt2wo1bCqauuo50JmoRZu4zsKzYIRDAAAEBwBAwAABEfAAAAAwREwAABAcAQMAAAQHAEDAAAEx2mqQIwpa+9JrbloWXoNABQVAQOIcdq+I6k1u6ek1wBAUTFFAgAAgiNgADEOLu7SwcVdiTWrtnZp1dbkGgAoKgIGEGN07nSNzp2eWLN013Qt3ZVcAwBFRcAAAADBcZBnFUItWgQgP1n+jkPV5C1rG8cvkha3XzULqWV9HRZyay2MYAAAgOAIGAAAIDgCBgAACI5jMIAYpz5+OLVmqD29BgCKioABxJh6/bbUmoX96TUAUFRMkQAAgOAIGAAAIDgCBhBjz+o+7Uk53354TZ+G13BOPgDEIWAAAIDgCBgAACA4AgYAAAiOgAEAAIJrmetgjF8khwVxADS7ahdSa+R+4/e5ouu4eur0XtUKtWhbs5go/z1kBAMAAATXMiMYQEhn3fRAas3Vb0mvAYCiImAAMc64b29qzY2z0msAoKiYIgEAAMERMIAYR+d06OicjsSaJTs7tGRncg0AFBVTJECMQ5dcICl5quTa20o1TJUAwIkYwQAAAMERMAAAQHA1BQwzW2BmPzKzR8zsqlCNAgAAza3qgGFmz5b0eUlvlXS+pCVmdn6ohgEAgOZVywjGHEmPuPuj7v5rSQOSFoVpFgAAaGa1BIxpksoPn388eg4AABScuXt1O5q9U9ICd/9A9Pi9kua6+4pxdcslLY8evlzSjySdI+nJahuNqtHv+aDf80G/54N+z0de/T7D3V8Qt6GW62Dsk1R+laFzo+eewd3XSVpX/pyZbXf37hreG1Wg3/NBv+eDfs8H/Z6PidjvtUyR/I+k88zsxWZ2qqT3SLolTLMAAEAzq3oEw92Pm9kKSbdJerakr7j7g8FaBgAAmlZNlwp3929L+nYVu65LL0Ed0O/5oN/zQb/ng37Px4Tr96oP8gQAAKiES4UDAIDg6hYw0i4jbmanmdnXo+33mllnvdpSNBn6/vVmdr+ZHY9ON0YAGfr9Y2b2AzN7wMzuMLMZebSz1WTo9w+b2ZCZ7TSzbVxxOIysS0WY2TvMzM1sQp3h0KwyfN77zeyn0ed9p5l9II92SpLcPfhNpYM+fyzpJZJOlbRL0vnjav5M0hein98j6ev1aEvRbhn7vlPSBZL+VdI7825zK9wy9nuvpOdFP/8pn/mG9fuksp8vlrQ173Y3+y1Lv0d1Z0i6W9L3JHXn3e5mv2X8vPdLuj7vtrp73UYwslxGfJGkDdHP35T0BjOzOrWnSFL73t2H3f0BSb/Lo4EtKku/3+nuv4gefk+la8egNln6/UjZw9MlceBZ7bIuFfEpSWsk/aqRjWthTbVER70CRpbLiP++xt2PSzos6ew6tadIuIR7Pk62398v6T/q2qJiyNTvZvYRM/uxpL+TdHmD2tbKUvvdzF4lqcPdb21kw1pc1u+Zd0RTsd80s46Y7Q3BQZ5Ag5nZZZK6JX0m77YUhbt/3t1fKulKSR/Puz2tzsyeJemzkq7Iuy0FtFlSp7tfIOl2PT1T0HD1ChhZLiP++xozO0XSZEkH69SeIsl0CXcEl6nfzeyNkv5K0sXufqxBbWtlJ/t5H5D09rq2qBjS+v0MSTMlDZrZsKTXSLqFAz1rlvp5d/eDZd8t/yJpdoPadoJ6BYwslxG/RdKy6Od3Svovj45QQU24hHs+UvvdzP5I0hdVChdP5NDGVpSl388re9gn6eEGtq9VJfa7ux9293PcvdPdO1U65uhid9+eT3NbRpbP+9SyhxdLeqiB7XuGmq7kWYlXuIy4mf2tpO3ufoukL0v6NzN7RNIhlToKNcrS92b2akmbJJ0paaGZfdLdX5ljs5texs/8ZyS1SfpGdDzzY+5+cW6NbgEZ+31FNHL0G0lP6en/sUGVMvY7AsvY75eb2cWSjqv039b+vNrLlTwBAEBwHOQJAACCI2AAAIDgCBgAACA4AgYAAAiOgAEAAIIjYAAAgOAIGAAAIDgCBgAACO7/Aes+my6aQXplAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram for each numeric feature\n",
    "for col in numeric_features:\n",
    "    fig = plt.figure(figsize=(9, 6))\n",
    "    ax = fig.gca()\n",
    "    feature = bike_data[col]\n",
    "    feature.hist(bins=100, ax = ax)\n",
    "    ax.axvline(feature.mean(), color='magenta', linestyle='dashed', linewidth=2)\n",
    "    ax.axvline(feature.median(), color='cyan', linestyle='dashed', linewidth=2)\n",
    "    ax.set_title(col)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The numeric features seem to be more *normally* distributed, with the mean and median nearer the middle of the range of values, coinciding with where the most commonly occurring values are.\n",
    "\n",
    "> **Note**: The distributions are not truly *normal* in the statistical sense, which would result in a smooth, symmetric \"bell-curve\" histogram with the mean and mode (the most common value) in the center; but they do generally indicate that most of the observations have a value somewhere near the middle.\n",
    "\n",
    "We've explored the distribution of the numeric values in the dataset, but what about the categorical features? These aren't continuous numbers on a scale, so we can't use histograms; but we can plot a bar chart showing the count of each discrete value for each category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "gather": {
     "logged": 1667468538711
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# plot a bar plot for each categorical feature count\n",
    "categorical_features = ['season','mnth','holiday','weekday','workingday','weathersit', 'day']\n",
    "\n",
    "for col in categorical_features:\n",
    "    counts = bike_data[col].value_counts().sort_index()\n",
    "    fig = plt.figure(figsize=(9, 6))\n",
    "    ax = fig.gca()\n",
    "    counts.plot.bar(ax = ax, color='steelblue')\n",
    "    ax.set_title(col + ' counts')\n",
    "    ax.set_xlabel(col) \n",
    "    ax.set_ylabel(\"Frequency\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of the categorical features show a more or less *uniform* distribution (meaning there's roughly the same number of rows for each category). Exceptions to this include:\n",
    "\n",
    "- **holiday**: There are many fewer days that are holidays than days that aren't.\n",
    "- **workingday**: There are more working days than non-working days.\n",
    "- **weathersit**: Most days are category *1* (clear), with category *2* (mist and cloud) the next most common. There are comparatively few category *3* (light rain or snow) days, and no category *4* (heavy rain, hail, or fog) days at all.\n",
    "\n",
    "Now that we know something about the distribution of the data in our columns, we can start to look for relationships between the features and the **rentals** label we want to be able to predict.\n",
    "\n",
    "For the numeric features, we can create scatter plots that show the intersection of feature and label values. We can also calculate the *correlation* statistic to quantify the apparent relationship.. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "gather": {
     "logged": 1667468540626
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in numeric_features:\n",
    "    fig = plt.figure(figsize=(9, 6))\n",
    "    ax = fig.gca()\n",
    "    feature = bike_data[col]\n",
    "    label = bike_data['rentals']\n",
    "    correlation = feature.corr(label)\n",
    "    plt.scatter(x=feature, y=label)\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('Bike Rentals')\n",
    "    ax.set_title('rentals vs ' + col + '- correlation: ' + str(correlation))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results aren't conclusive, but if you look closely at the scatter plots for **temp** and **atemp**, you can see a vague diagonal trend showing that higher rental counts tend to coincide with higher temperatures; and a correlation value of just over 0.5 for both of these features supports this observation. Conversely, the plots for **hum** and **windspeed** show a slightly negative correlation, indicating that there are fewer rentals on days with high humidity or windspeed.\n",
    "\n",
    "Now let's compare the categorical features to the label. We'll do this by creating box plots that show the distribution of rental counts for each category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "gather": {
     "logged": 1667468545254
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a boxplot for the label by each categorical feature\n",
    "for col in categorical_features:\n",
    "    fig = plt.figure(figsize=(9, 6))\n",
    "    ax = fig.gca()\n",
    "    bike_data.boxplot(column = 'rentals', by = col, ax = ax)\n",
    "    ax.set_title('Label by ' + col)\n",
    "    ax.set_ylabel(\"Bike Rentals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots show some variance in the relationship between some category values and rentals. For example, there's a clear difference in the distribution of rentals on weekends (**weekday** 0 or 6) and those during the working week (**weekday** 1 to 5). Similarly, there are notable differences for **holiday** and **workingday** categories. There's a noticeable trend that shows different rental distributions in summer and fall months compared to spring and winter months. The **weathersit** category also seems to make a difference in rental distribution. The **day** feature we created for the day of the month shows little variation, indicating that it's probably not predictive of the number of rentals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a Regression Model\n",
    "\n",
    "Now that we've explored the data, it's time to use it to train a regression model that uses the features we've identified as potentially predictive to predict the **rentals** label.  The first thing we need to do is to separate the features we want to use to train the model from the label we want it to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "gather": {
     "logged": 1667468545488
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features:\n",
      "[[1.        1.        0.        6.        0.        2.        0.344167\n",
      "  0.363625  0.805833  0.160446 ]\n",
      " [1.        1.        0.        0.        0.        2.        0.363478\n",
      "  0.353739  0.696087  0.248539 ]\n",
      " [1.        1.        0.        1.        1.        1.        0.196364\n",
      "  0.189405  0.437273  0.248309 ]\n",
      " [1.        1.        0.        2.        1.        1.        0.2\n",
      "  0.212122  0.590435  0.160296 ]\n",
      " [1.        1.        0.        3.        1.        1.        0.226957\n",
      "  0.22927   0.436957  0.1869   ]\n",
      " [1.        1.        0.        4.        1.        1.        0.204348\n",
      "  0.233209  0.518261  0.0895652]\n",
      " [1.        1.        0.        5.        1.        2.        0.196522\n",
      "  0.208839  0.498696  0.168726 ]\n",
      " [1.        1.        0.        6.        0.        2.        0.165\n",
      "  0.162254  0.535833  0.266804 ]\n",
      " [1.        1.        0.        0.        0.        1.        0.138333\n",
      "  0.116175  0.434167  0.36195  ]\n",
      " [1.        1.        0.        1.        1.        1.        0.150833\n",
      "  0.150888  0.482917  0.223267 ]]\n",
      "\n",
      "Labels:\n",
      "[331 131 120 108  82  88 148  68  54  41]\n"
     ]
    }
   ],
   "source": [
    "# Separate features and labels\n",
    "X, y = bike_data[['season','mnth', 'holiday','weekday','workingday','weathersit','temp', 'atemp', 'hum', 'windspeed']].values, bike_data['rentals'].values\n",
    "print('Features:',X[:10], '\\nLabels:', y[:10], sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After separating the dataset, we now have numpy arrays named **X** containing the features, and **y** containing the labels.\n",
    "\n",
    "We *could* train a model using all of the data; but it's common practice in supervised learning to split the data into two subsets; a (typically larger) set with which to train the model, and a smaller \"hold-back\" set with which to validate the trained model. This enables us to evaluate how well the model performs when used with the validation dataset by comparing the predicted labels to the known labels. It's important to split the data *randomly* (rather than say, taking the first 70% of the data for training and keeping the rest for validation). This helps ensure that the two subsets of data are statistically comparable (so we validate the model with data that has a similar statistical distribution to the data on which it was trained).\n",
    "\n",
    "To randomly split the data, we'll use the **train_test_split** function in the **scikit-learn** library. This library is one of the most widely used machine learning packages for Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "gather": {
     "logged": 1667468546584
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set: 511 rows\n",
      "Test Set: 220 rows\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split data 70%-30% into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n",
    "\n",
    "print ('Training Set: %d rows\\nTest Set: %d rows' % (X_train.shape[0], X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the following four datasets:\n",
    "\n",
    "- **X_train**: The feature values we'll use to train the model\n",
    "- **y_train**: The corresponding labels we'll use to train the model\n",
    "- **X_test**: The feature values we'll use to validate the model\n",
    "- **y_test**: The corresponding labels we'll use to validate the model\n",
    "\n",
    "Now we're ready to train a model by fitting a suitable regression algorithm to the training data. We'll use a *linear regression* algorithm, a common starting point for regression that works by trying to find a linear relationship between the *X* values and the *y* label. The resulting model is a function that conceptually defines a line where every possible X and y value combination intersect.\n",
    "\n",
    "In Scikit-Learn, training algorithms are encapsulated in *estimators*, and in this case we'll use the **LinearRegression** estimator to train a linear regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "gather": {
     "logged": 1667468547048
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Fit a linear regression model on the training set\n",
    "model = LinearRegression().fit(X_train, y_train)\n",
    "print (model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the Trained Model\n",
    "\n",
    "Now that we've trained the model, we can use it to predict rental counts for the features we held back in our validation dataset. Then we can compare these predictions to the actual label values to evaluate how well (or not!) the model is working."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "gather": {
     "logged": 1667468547319
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted labels:  [1896. 1184. 1007.  -28.  314.  385.  475.  590. 1476.  -22.]\n",
      "Actual labels   :  [2418  754  222   47  244  145  240  555 3252   38]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "np.set_printoptions(suppress=True)\n",
    "print('Predicted labels: ', np.round(predictions)[:10])\n",
    "print('Actual labels   : ' ,y_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing each prediction with its corresponding \"ground truth\" actual value isn't a very efficient way to determine how well the model is predicting. Let's see if we can get a better indication by visualizing a scatter plot that compares the predictions to the actual labels. We'll also overlay a trend line to get a general sense for how well the predicted labels align with the true labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "gather": {
     "logged": 1667468547781
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a definite diagonal trend, and the intersections of the predicted and actual values are generally following the path of the trend line; but there's a fair amount of difference between the ideal function represented by the line and the results. This variance represents the *residuals* of the model - in other words, the difference between the label predicted when the model applies the coefficients it learned during training to the validation data, and the actual value of the validation label. These residuals when evaluated from the validation data indicate the expected level of *error* when the model is used with new data for which the label is unknown.\n",
    "\n",
    "You can quantify the residuals by calculating a number of commonly used evaluation metrics. We'll focus on the following three:\n",
    "\n",
    "- **Mean Square Error (MSE)**: The mean of the squared differences between predicted and actual values. This yields a relative metric in which the smaller the value, the better the fit of the model\n",
    "- **Root Mean Square Error (RMSE)**: The square root of the MSE. This yields an absolute metric in the same unit as the label (in this case, numbers of rentals). The smaller the value, the better the model (in a simplistic sense, it represents the average number of rentals by which the predictions are wrong!)\n",
    "- **Coefficient of Determination (usually known as *R-squared* or R<sup>2</sup>)**: A relative metric in which the higher the value, the better the fit of the model. In essence, this metric represents how much of the variance between predicted and actual label values the model is able to explain.\n",
    "\n",
    "> **Note**: You can find out more about these and other metrics for evaluating regression models in the [Scikit-Learn documentation](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)\n",
    "\n",
    "Let's use Scikit-Learn to calculate these metrics for our model, based on the predictions it generated for the validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "gather": {
     "logged": 1667468548080
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 201972.55947035592\n",
      "RMSE: 449.4135728595165\n",
      "R2: 0.6040454736919191\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we've quantified the ability of our model to predict the number of rentals. It definitely has *some* predictive power, but we can probably do better!\n",
    "\n",
    "## Experiment with Algorithms\n",
    "\n",
    "The linear regression algorithm we used to train the model has some predictive capability, but there are many kinds of regression algorithm we could try, including:\n",
    "\n",
    "- **Linear algorithms**: Not just the Linear Regression algorithm we used above (which is technically an *Ordinary Least Squares* algorithm), but other variants such as *Lasso* and *Ridge*.\n",
    "- **Tree-based algorithms**: Algorithms that build a decision tree to reach a prediction.\n",
    "- **Ensemble algorithms**: Algorithms that combine the outputs of multiple base algorithms to improve generalizability.\n",
    "\n",
    "> **Note**: For a full list of Scikit-Learn estimators that encapsulate algorithms for supervised machine learning, see the [Scikit-Learn documentation](https://scikit-learn.org/stable/supervised_learning.html). There are many algorithms to choose from, but for most real-world scenarios, the [Scikit-Learn estimator cheat sheet](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html) can help you find a suitable starting point. \n",
    "\n",
    "### Try Another Linear Algorithm\n",
    "\n",
    "Let's try training our regression model by using a **Lasso** algorithm. We can do this by just changing the estimator in the training code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "gather": {
     "logged": 1667468548446
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
      "      normalize=False, positive=False, precompute=False, random_state=None,\n",
      "      selection='cyclic', tol=0.0001, warm_start=False) \n",
      "\n",
      "MSE: 201155.70593338404\n",
      "RMSE: 448.5038527519959\n",
      "R2: 0.6056468637824488\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# Fit a lasso model on the training set\n",
    "model = Lasso().fit(X_train, y_train)\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Evaluate the model using the test data\n",
    "predictions = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try a Decision Tree Algorithm\n",
    "\n",
    "As an alternative to a linear model, there's a category of algorithms for machine learning that uses a tree-based approach in which the features in the dataset are examined in a series of evaluations, each of which results in a *branch* in a *decision tree* based on the feature value. At the end of each series of branches are leaf-nodes with the predicted label value based on the feature values.\n",
    "\n",
    "It's easiest to see how this works with an example. Let's train a Decision Tree regression model using the bike rental data. After training the model, the code below will print the model definition and a text representation of the tree it uses to predict label values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "gather": {
     "logged": 1667468548901
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
      "                      max_features=None, max_leaf_nodes=None,\n",
      "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                      min_samples_leaf=1, min_samples_split=2,\n",
      "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
      "                      random_state=None, splitter='best') \n",
      "\n",
      "|--- feature_6 <= 0.45\n",
      "|   |--- feature_4 <= 0.50\n",
      "|   |   |--- feature_7 <= 0.32\n",
      "|   |   |   |--- feature_8 <= 0.41\n",
      "|   |   |   |   |--- feature_1 <= 2.50\n",
      "|   |   |   |   |   |--- feature_9 <= 0.33\n",
      "|   |   |   |   |   |   |--- feature_1 <= 1.50\n",
      "|   |   |   |   |   |   |   |--- value: [558.00]\n",
      "|   |   |   |   |   |   |--- feature_1 >  1.50\n",
      "|   |   |   |   |   |   |   |--- value: [515.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.33\n",
      "|   |   |   |   |   |   |--- value: [317.00]\n",
      "|   |   |   |   |--- feature_1 >  2.50\n",
      "|   |   |   |   |   |--- feature_9 <= 0.28\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.28\n",
      "|   |   |   |   |   |   |   |--- value: [981.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.28\n",
      "|   |   |   |   |   |   |   |--- value: [968.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.28\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.36\n",
      "|   |   |   |   |   |   |   |--- value: [710.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.36\n",
      "|   |   |   |   |   |   |   |--- value: [532.00]\n",
      "|   |   |   |--- feature_8 >  0.41\n",
      "|   |   |   |   |--- feature_7 <= 0.25\n",
      "|   |   |   |   |   |--- feature_6 <= 0.18\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.43\n",
      "|   |   |   |   |   |   |   |--- value: [284.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.43\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.45\n",
      "|   |   |   |   |   |   |   |   |--- value: [150.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.45\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.21\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [117.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.21\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.24\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [67.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.24\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [68.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [73.00]\n",
      "|   |   |   |   |   |--- feature_6 >  0.18\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.11\n",
      "|   |   |   |   |   |   |   |   |--- value: [140.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.11\n",
      "|   |   |   |   |   |   |   |   |--- value: [123.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.19\n",
      "|   |   |   |   |   |   |   |   |--- value: [333.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.19\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.53\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [251.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.21\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [217.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.21\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [205.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.53\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [288.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [275.00]\n",
      "|   |   |   |   |--- feature_7 >  0.25\n",
      "|   |   |   |   |   |--- feature_9 <= 0.11\n",
      "|   |   |   |   |   |   |--- value: [706.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.11\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.54\n",
      "|   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [309.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.26\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [408.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [440.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [502.00]\n",
      "|   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [618.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.54\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.29\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [354.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [318.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.29\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 7.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [195.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  7.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [155.00]\n",
      "|   |   |--- feature_7 >  0.32\n",
      "|   |   |   |--- feature_9 <= 0.25\n",
      "|   |   |   |   |--- feature_7 <= 0.38\n",
      "|   |   |   |   |   |--- feature_7 <= 0.36\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.36\n",
      "|   |   |   |   |   |   |   |--- feature_1 <= 10.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.34\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.20\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.20\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1047.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [724.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.34\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.83\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.35\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.35\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [694.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.83\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [879.00]\n",
      "|   |   |   |   |   |   |   |--- feature_1 >  10.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.57\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.33\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1156.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.33\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [943.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.57\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.66\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [955.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.66\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.33\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [767.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.33\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [922.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.36\n",
      "|   |   |   |   |   |   |   |--- value: [1658.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.36\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.35\n",
      "|   |   |   |   |   |   |   |--- value: [331.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.35\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.13\n",
      "|   |   |   |   |   |   |   |   |--- value: [538.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.13\n",
      "|   |   |   |   |   |   |   |   |--- value: [560.00]\n",
      "|   |   |   |   |--- feature_7 >  0.38\n",
      "|   |   |   |   |   |--- feature_9 <= 0.24\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.67\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.58\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2252.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.58\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2290.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.67\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 10.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1619.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  10.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1249.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1153.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |--- value: [1651.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.51\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.20\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.78\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1070.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.78\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1188.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.20\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 8.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [642.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  8.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [665.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.24\n",
      "|   |   |   |   |   |   |--- value: [2301.00]\n",
      "|   |   |   |--- feature_9 >  0.25\n",
      "|   |   |   |   |--- feature_8 <= 0.81\n",
      "|   |   |   |   |   |--- feature_8 <= 0.49\n",
      "|   |   |   |   |   |   |--- value: [397.00]\n",
      "|   |   |   |   |   |--- feature_8 >  0.49\n",
      "|   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |--- value: [982.00]\n",
      "|   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.77\n",
      "|   |   |   |   |   |   |   |   |--- value: [480.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.77\n",
      "|   |   |   |   |   |   |   |   |--- value: [640.00]\n",
      "|   |   |   |   |--- feature_8 >  0.81\n",
      "|   |   |   |   |   |--- feature_9 <= 0.34\n",
      "|   |   |   |   |   |   |--- value: [121.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.34\n",
      "|   |   |   |   |   |   |--- value: [120.00]\n",
      "|   |--- feature_4 >  0.50\n",
      "|   |   |--- feature_6 <= 0.34\n",
      "|   |   |   |--- feature_1 <= 2.50\n",
      "|   |   |   |   |--- feature_7 <= 0.29\n",
      "|   |   |   |   |   |--- feature_7 <= 0.19\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.14\n",
      "|   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [89.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.47\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [86.00]\n",
      "|   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [95.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.14\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.46\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.18\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [75.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.18\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [61.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.46\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.30\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [38.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [41.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.57\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.57\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [42.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.30\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [25.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.19\n",
      "|   |   |   |   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [186.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.19\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.19\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [82.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.26\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.82\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.31\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [72.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.31\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [64.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.82\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [34.00]\n",
      "|   |   |   |   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |--- value: [115.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.47\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.24\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [148.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [149.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.24\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [174.00]\n",
      "|   |   |   |   |--- feature_7 >  0.29\n",
      "|   |   |   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.32\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.31\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [206.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.62\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [163.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.62\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [173.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.31\n",
      "|   |   |   |   |   |   |   |   |--- value: [218.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.32\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |   |   |--- value: [74.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |   |   |--- value: [135.00]\n",
      "|   |   |   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.30\n",
      "|   |   |   |   |   |   |   |--- value: [227.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.30\n",
      "|   |   |   |   |   |   |   |--- feature_1 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [307.00]\n",
      "|   |   |   |   |   |   |   |--- feature_1 >  1.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [310.00]\n",
      "|   |   |   |--- feature_1 >  2.50\n",
      "|   |   |   |   |--- feature_9 <= 0.20\n",
      "|   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.06\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 3.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [337.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  3.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [362.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.06\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [143.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.27\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [174.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.27\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [178.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  3.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.29\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [243.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [254.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.29\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.66\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [268.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.66\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [261.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.64\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.45\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.28\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [245.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.28\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [316.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.45\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.14\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [491.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [429.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.14\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.30\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.30\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [359.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.64\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.65\n",
      "|   |   |   |   |   |   |   |   |--- value: [168.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.65\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.72\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.32\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [289.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.32\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [314.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.72\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [349.00]\n",
      "|   |   |   |   |--- feature_9 >  0.20\n",
      "|   |   |   |   |   |--- feature_8 <= 0.78\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.23\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.21\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.48\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [221.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [222.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.48\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [198.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.21\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [123.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [137.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.75\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [177.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.75\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [191.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.23\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.56\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.25\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [203.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.25\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.43\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [317.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.43\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [326.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.56\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.62\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.27\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [150.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.27\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [139.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.62\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.33\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [195.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.33\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [247.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.23\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [456.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.23\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.31\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [300.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [307.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.31\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [247.00]\n",
      "|   |   |   |   |   |--- feature_8 >  0.78\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.28\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 3.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.86\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [166.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.86\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [179.00]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  3.00\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.81\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [123.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.81\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [87.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.28\n",
      "|   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |--- feature_6 >  0.34\n",
      "|   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |--- feature_8 <= 0.48\n",
      "|   |   |   |   |   |--- feature_7 <= 0.42\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.44\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.39\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.23\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [229.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.23\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 6.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [324.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  6.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [340.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.39\n",
      "|   |   |   |   |   |   |   |   |--- value: [208.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.44\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [482.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [518.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.47\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [413.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  3.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [663.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.42\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.40\n",
      "|   |   |   |   |   |   |   |--- value: [1192.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.40\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.32\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.44\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [834.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.44\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [819.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.32\n",
      "|   |   |   |   |   |   |   |   |--- value: [795.00]\n",
      "|   |   |   |   |--- feature_8 >  0.48\n",
      "|   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.55\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.30\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.36\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [199.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [141.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.36\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.25\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [259.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.25\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [253.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.30\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.33\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [331.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.33\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [432.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.55\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.65\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.19\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 3.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [571.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  3.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [615.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.19\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [460.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [394.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.65\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.10\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.07\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [305.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.07\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [376.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [370.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.10\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [410.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [433.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [439.00]\n",
      "|   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.38\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.39\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 3.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [534.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  3.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [466.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.39\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [330.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.35\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.82\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.37\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.37\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [269.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.82\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [203.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.35\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [127.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.38\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 3.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [190.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  3.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [233.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.26\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.75\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [167.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [181.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [255.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.75\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_5 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 6.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [81.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  6.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [112.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_5 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 2.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  2.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [50.00]\n",
      "|   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |--- feature_8 <= 0.39\n",
      "|   |   |   |   |   |--- value: [1807.00]\n",
      "|   |   |   |   |--- feature_8 >  0.39\n",
      "|   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.16\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.37\n",
      "|   |   |   |   |   |   |   |   |--- value: [1603.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.37\n",
      "|   |   |   |   |   |   |   |   |--- value: [1095.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.16\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.35\n",
      "|   |   |   |   |   |   |   |   |--- value: [484.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.35\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.36\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [709.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.36\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [618.00]\n",
      "|   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.37\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.58\n",
      "|   |   |   |   |   |   |   |   |--- value: [178.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.58\n",
      "|   |   |   |   |   |   |   |   |--- value: [246.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.37\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.39\n",
      "|   |   |   |   |   |   |   |   |--- value: [796.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.39\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.42\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [447.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [470.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.42\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [548.00]\n",
      "|--- feature_6 >  0.45\n",
      "|   |--- feature_4 <= 0.50\n",
      "|   |   |--- feature_8 <= 0.83\n",
      "|   |   |   |--- feature_1 <= 10.50\n",
      "|   |   |   |   |--- feature_9 <= 0.28\n",
      "|   |   |   |   |   |--- feature_6 <= 0.81\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.67\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.09\n",
      "|   |   |   |   |   |   |   |   |--- value: [3065.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.09\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.66\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.66\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3031.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.67\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.67\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [2207.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1138.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 4.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3155.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  4.00\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.67\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.74\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1298.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1249.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_2 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1521.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_2 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1549.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.74\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1920.00]\n",
      "|   |   |   |   |   |--- feature_6 >  0.81\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.80\n",
      "|   |   |   |   |   |   |   |--- value: [1203.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.80\n",
      "|   |   |   |   |   |   |   |--- value: [987.00]\n",
      "|   |   |   |   |--- feature_9 >  0.28\n",
      "|   |   |   |   |   |--- feature_8 <= 0.52\n",
      "|   |   |   |   |   |   |--- value: [1558.00]\n",
      "|   |   |   |   |   |--- feature_8 >  0.52\n",
      "|   |   |   |   |   |   |--- feature_1 <= 7.00\n",
      "|   |   |   |   |   |   |   |--- value: [1198.00]\n",
      "|   |   |   |   |   |   |--- feature_1 >  7.00\n",
      "|   |   |   |   |   |   |   |--- value: [998.00]\n",
      "|   |   |   |--- feature_1 >  10.50\n",
      "|   |   |   |   |--- feature_9 <= 0.18\n",
      "|   |   |   |   |   |--- value: [1097.00]\n",
      "|   |   |   |   |--- feature_9 >  0.18\n",
      "|   |   |   |   |   |--- value: [787.00]\n",
      "|   |   |--- feature_8 >  0.83\n",
      "|   |   |   |--- feature_6 <= 0.67\n",
      "|   |   |   |   |--- feature_7 <= 0.52\n",
      "|   |   |   |   |   |--- feature_9 <= 0.21\n",
      "|   |   |   |   |   |   |--- feature_1 <= 4.00\n",
      "|   |   |   |   |   |   |   |--- value: [1033.00]\n",
      "|   |   |   |   |   |   |--- feature_1 >  4.00\n",
      "|   |   |   |   |   |   |   |--- value: [902.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.21\n",
      "|   |   |   |   |   |   |--- value: [1462.00]\n",
      "|   |   |   |   |--- feature_7 >  0.52\n",
      "|   |   |   |   |   |--- feature_6 <= 0.61\n",
      "|   |   |   |   |   |   |--- value: [1582.00]\n",
      "|   |   |   |   |   |--- feature_6 >  0.61\n",
      "|   |   |   |   |   |   |--- value: [1483.00]\n",
      "|   |   |   |--- feature_6 >  0.67\n",
      "|   |   |   |   |--- value: [226.00]\n",
      "|   |--- feature_4 >  0.50\n",
      "|   |   |--- feature_8 <= 0.83\n",
      "|   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |--- feature_7 <= 0.53\n",
      "|   |   |   |   |   |--- feature_9 <= 0.10\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.47\n",
      "|   |   |   |   |   |   |   |--- value: [1348.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.47\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [830.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [846.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [763.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.51\n",
      "|   |   |   |   |   |   |   |   |--- value: [1122.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.10\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.23\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.64\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [838.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [922.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.47\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 7.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  7.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.64\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.74\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [637.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.74\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [699.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [486.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [409.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [655.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.46\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [516.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.46\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.54\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [695.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [735.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.54\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [550.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [559.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.23\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 3.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [745.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [614.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  3.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [471.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.47\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [905.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [834.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1008.00]\n",
      "|   |   |   |   |--- feature_7 >  0.53\n",
      "|   |   |   |   |   |--- feature_9 <= 0.13\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.72\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.68\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.82\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.09\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.63\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.63\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.09\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.82\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1334.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.68\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1177.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1363.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.68\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [989.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.68\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1233.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.72\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.78\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.76\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [568.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.76\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [673.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.78\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [921.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [872.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [778.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.13\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.53\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.81\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1281.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 6.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  6.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 6.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1242.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  6.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1032.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.81\n",
      "|   |   |   |   |   |   |   |   |--- value: [1405.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.53\n",
      "|   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.65\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1198.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.65\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1128.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.73\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.73\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.57\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [662.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.57\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [606.00]\n",
      "|   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.55\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.55\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.20\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [653.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.20\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [630.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.19\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.19\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 8.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  8.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [428.00]\n",
      "|   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |--- feature_1 <= 4.00\n",
      "|   |   |   |   |   |--- value: [2469.00]\n",
      "|   |   |   |   |--- feature_1 >  4.00\n",
      "|   |   |   |   |   |--- feature_8 <= 0.72\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.08\n",
      "|   |   |   |   |   |   |   |   |--- value: [1325.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.08\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.65\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 8.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1511.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  8.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1516.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1379.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.83\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 6.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.52\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.36\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [898.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.36\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.52\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  6.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1366.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.83\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 6.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [829.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  6.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [670.00]\n",
      "|   |   |   |   |   |--- feature_8 >  0.72\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.20\n",
      "|   |   |   |   |   |   |   |--- feature_1 <= 5.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.13\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [909.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.13\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1417.00]\n",
      "|   |   |   |   |   |   |   |--- feature_1 >  5.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1182.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.75\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.75\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1045.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.20\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.74\n",
      "|   |   |   |   |   |   |   |   |--- value: [529.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.74\n",
      "|   |   |   |   |   |   |   |   |--- value: [533.00]\n",
      "|   |   |--- feature_8 >  0.83\n",
      "|   |   |   |--- feature_5 <= 2.50\n",
      "|   |   |   |   |--- feature_9 <= 0.10\n",
      "|   |   |   |   |   |--- value: [258.00]\n",
      "|   |   |   |   |--- feature_9 >  0.10\n",
      "|   |   |   |   |   |--- feature_1 <= 5.50\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.53\n",
      "|   |   |   |   |   |   |   |--- value: [692.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.53\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [678.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [547.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [536.00]\n",
      "|   |   |   |   |   |--- feature_1 >  5.50\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.89\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.60\n",
      "|   |   |   |   |   |   |   |   |--- value: [438.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.60\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [477.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [480.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.89\n",
      "|   |   |   |   |   |   |   |--- value: [555.00]\n",
      "|   |   |   |--- feature_5 >  2.50\n",
      "|   |   |   |   |--- feature_6 <= 0.50\n",
      "|   |   |   |   |   |--- value: [126.00]\n",
      "|   |   |   |   |--- feature_6 >  0.50\n",
      "|   |   |   |   |   |--- feature_7 <= 0.53\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.25\n",
      "|   |   |   |   |   |   |   |--- value: [254.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.25\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [204.00]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  3.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [217.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.53\n",
      "|   |   |   |   |   |   |--- value: [315.00]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import export_text\n",
    "\n",
    "# Train the model\n",
    "model = DecisionTreeRegressor().fit(X_train, y_train)\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Visualize the model tree\n",
    "tree = export_text(model)\n",
    "print(tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we have a tree-based model; but is it any good? Let's evaluate it with the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "gather": {
     "logged": 1667468549326
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 243384.30454545454\n",
      "RMSE: 493.3399482562248\n",
      "R2: 0.5228603466241584\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "predictions = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tree-based model doesn't seem to have improved over the linear model, so what else could we try?\n",
    "\n",
    "### Try an Ensemble Algorithm\n",
    "\n",
    "Ensemble algorithms work by combining multiple base estimators to produce an optimal model, either by applying an aggregate function to a collection of base models (sometimes referred to a *bagging*) or by building a sequence of models that build on one another to improve predictive performance (referred to as *boosting*).\n",
    "\n",
    "For example, let's try a Random Forest model, which applies an averaging function to multiple Decision Tree models for a better overall model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "gather": {
     "logged": 1667468550644
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
      "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "                      max_samples=None, min_impurity_decrease=0.0,\n",
      "                      min_impurity_split=None, min_samples_leaf=1,\n",
      "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
      "                      random_state=None, verbose=0, warm_start=False) \n",
      "\n",
      "MSE: 109455.92989545454\n",
      "RMSE: 330.8412457591322\n",
      "R2: 0.7854185192928335\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Train the model\n",
    "model = RandomForestRegressor().fit(X_train, y_train)\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Evaluate the model using the test data\n",
    "predictions = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For good measure, let's also try a *boosting* ensemble algorithm. We'll use a Gradient Boosting estimator, which like a Random Forest algorithm builds multiple trees, but instead of building them all independently and taking the average result, each tree is built on the outputs of the previous one in an attempt to incrementally reduce the *loss* (error) in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "gather": {
     "logged": 1667468551340
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
      "                          init=None, learning_rate=0.1, loss='ls', max_depth=3,\n",
      "                          max_features=None, max_leaf_nodes=None,\n",
      "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                          min_samples_leaf=1, min_samples_split=2,\n",
      "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                          n_iter_no_change=None, presort='deprecated',\n",
      "                          random_state=None, subsample=1.0, tol=0.0001,\n",
      "                          validation_fraction=0.1, verbose=0, warm_start=False) \n",
      "\n",
      "MSE: 104254.3411763734\n",
      "RMSE: 322.88440838227757\n",
      "R2: 0.7956159075059364\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train the model\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# Fit a lasso model on the training set\n",
    "model = GradientBoostingRegressor().fit(X_train, y_train)\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Evaluate the model using the test data\n",
    "predictions = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimize Hyperparameters\n",
    "\n",
    "Take a look at the **GradientBoostingRegressor** estimator definition in the output above, and note that it, like the other estimators we tried previously, includes a large number of parameters that control the way the model is trained. In machine learning, the term *parameters* refers to values that can be determined from data; values that you specify to affect the behavior of a training algorithm are more correctly referred to as *hyperparameters*.\n",
    "\n",
    "The specific hyperparameters for an estimator vary based on the algorithm that the estimator encapsulates. In the case of the **GradientBoostingRegressor** estimator, the algorithm is an ensemble that combines multiple decision trees to create an overall predictive model. You can learn about the hyperparameters for this estimator in the [Scikit-Learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html).\n",
    "\n",
    "We won't go into the details of each hyperparameter here, but they work together to affect the way the algorithm trains a model. In many cases, the default values provided by Scikit-Learn will work well; but there may be some advantage in modifying hyperparameters to get better predictive performance or reduce training time.\n",
    "\n",
    "So how do you know what hyperparameter values you should use? Well, in the absence of a deep understanding of how the underlying algorithm works, you'll need to experiment. Fortunately, SciKit-Learn provides a way to *tune* hyperparameters by trying multiple combinations and finding the best result for a given performance metric.\n",
    "\n",
    "Let's try using a *grid search* approach to try combinations from a grid of possible values for the **learning_rate** and **n_estimators** hyperparameters of the **GradientBoostingRegressor** estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "gather": {
     "logged": 1667468558244
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter combination: {'learning_rate': 0.1, 'n_estimators': 100} \n",
      "\n",
      "GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
      "                          init=None, learning_rate=0.1, loss='ls', max_depth=3,\n",
      "                          max_features=None, max_leaf_nodes=None,\n",
      "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                          min_samples_leaf=1, min_samples_split=2,\n",
      "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                          n_iter_no_change=None, presort='deprecated',\n",
      "                          random_state=None, subsample=1.0, tol=0.0001,\n",
      "                          validation_fraction=0.1, verbose=0, warm_start=False) \n",
      "\n",
      "MSE: 104041.92658402251\n",
      "RMSE: 322.55530779080743\n",
      "R2: 0.7960323329823267\n"
     ]
    },
    {
     "data": {
      "image/png": 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uuptWVRoi/j6Fuu+VFLZdCEyBGN2WfB6YuT4ACv3AqLpQWjregzASPYjfAXYBmoIHAoEWdXHKOureAbSqAtF/n0Ld90oJ2y4U5gMxjBzINXqr0NFeVRVK6wm7B5nEPogV+CqwHe3K4x4/nqE84vwUYfcujLC/T6Hue6UEbxQKUyCGkQO5PgAK/cCoilDaDLJ91tgH8XTcU+pav/59nOIY03HXbMo6eO9ylblQ971SgjcKhZmwjKqkXI7IXE0ZxTA5lcJXUYoQYSDS58ArwJDA+q7AM0DMbUuayzJuRAOjp8yNlCklcyaFuO+VFLxRCGwGYlQd5QypzNWUUU0mp1KHCP/2tOHMm/SZ9IdyC/Bx0pXHEuAlYpUH5PZ2H2fOKubfpxpnkHHYDMSoOsrpiMw1eitqf4DRU+ZWVChn2UOELwN+GFi/Hjgn+XVyebsPytTY1EyNSGwUViGp2GTMPBD10QddiZEjR+r8+fPLLYZRJIZMmkXYt1aA5VOOLbU4ORMWlVRXW1OSN9E4E1XZ7uv/gIMD6ycBfycv+0hXyrEoByKyQFVHJt3fZiBG1VGNoaxByjWDyhZSXPL7uhYYCGwOjK3BlSHJk670dl8NmA/EqDqqya8QRrlCObNFKZXsviowHvgQ7crjIT/eCeVhlB6bgRhVR6nKbhSLcs2gsimuktzXa3E5HSkmAxen71JqM5SZvfLHFIhRlVSzqaJcoZxJFFfR7uujwEGB9Y8CjwO903crdamPrlZapNQUzYQlIjuLyAMi8qyILBGR7/jx/iIyR0Re9L/7+XERkStFZJmIPCUiBwTOdZbf/0UROatYMhtGKShXKGdZTH/v4bzwQeXxJLCYDsoDSt+npVL6wlQrxZyBbAYuUNUnRWQbYIGIzAHOBu5X1SkiMgmYBFwIHA0M9T+jgKuBUSLSHzfJHYmzki4QkZmquq6IshtGGoU2c5RjBtVZE1XO9+BgXIRVih8DP4+/Rqn9Q12ttEipKZoCUdVVwCq/vEFEnsNV7T8BONTvdiPwIE6BnADcpC6u+FERqReRHfy+c1R1LYBXQmOBW4olu2EE6UpmjnwVV0734DrgK4H1bYD1uJlIFkrtH6r2iL5yU5IoLBEZDIwAHgMGeuUC8AYukA+cclkROGylH4saz7zGBBGZLyLz16xZU1D5je6NmTkS3oPlOCURVB6rcFV0EygPKL2Zrdoj+spN0RWIiGwN3AGcr6rvBLf52UZBMhlV9RpVHamqIwcMGFCIUxoGYGYOyHIPWnEKYtfAhum4/+yP5HadUvuHulppkVJT1CgsEanFKY+/qep0P7xaRHZQ1VXeRPWmH28Edg4cvpMfa6Td5JUaf7CYchvdm0xbf33fWtZtbOmwX3cyc0SZei5/cP/0p8iJOOXRCUrtH6rmiL5yU8woLMFZQ59T1csDm2YCqUiqs4B/BsbP9NFYBwLrvalrNnCUiPTzEVtH+THDKDhhBQXffX8ztTXpNpjuZubINPUc9OqHeOWXx3LiYzu17/QBnVYeRnVRzBnIaOCLwNMissiP/RCYAtwuIl8GXgVO9dvuBo4BlgEb8WXUVHWtiPwceMLv97OUQ90wCk2Yrb9li1JfV8tWvXt222Sz1Ge9esZLzP7Zp9I3Pg3sW3qZjPKTtZiiiJwC3OMjqX4MHABcqqpPlkLAfLBiika+VHuhxqKyFxCMG/gVMLFMshhFIddiiklMWD/xyuMQXPv663A5GobR5ehqHeMKwhU4DZpSHrvgHOSmPLo9SRRIaj5/LHCNqs4CehVPJMMoHxbWGeB5nOI4PzD2Nq5boGGQzAfSKCJ/BI4EfikivbEqvkYXpdoLNRaEFjq+It5DaB9yo3uTxAfSF5f5/bSqvuhDb/dT1XtLIWA+mA/EqESqourreODWwPo5uM6ARregYA2lfA2qFA8Gxj4A7OlsGDlQ8eVQ7gKOzxhrwep1G7HEfT0W4FxlYUUIlPS8U8MwYihnH/dY1gAfzhh7Edi9DLIYVUekAlHVIaUUxDC6MhVXDkWBATineIqrgXPLI45RnWR1hvvM8DNE5Cd+fZCIfKL4ohlG16GiwoN/jvvPTymPETiFYsrDyJEk0VT/D9cO5vN+fQNwVdEkMowuSEWEBy/EGaR/Ghh7B9fgyTDyIImLbJSqHiAiCwFUdZ2IWB6IYeRAWcOD3wcyJzr/AT5Z/EsbXZskCqRFRGrwZddFZACwpahSGUaFUMjQ27JUfR1LeunR84FppRXB6LokUSBXAncCA0XkF8DJuOaUhtGlqfjQ2zhuxeV0BGnFUoCNgpJVgajq30RkAXC4Hxqnqs8VVyzDKD8VG3obx0rSu+oAvBYyZhgFIOn7SF+gxu/fjavKGd2JqBDbxqZmhkyaxegpc5mxsLHEUkWguP/OoKL4qx835WEUiSRhvD8FbgT6A9sDf/Zl3Q2j7MxY2MjoKXOL8kCPC7FNNZu6aPrT5VciE3H/yamqRIf75S+UTSKjm5CkFtZSYH9Vfd+v1wGLVLViy5NaLazuQaaPAlxobC49reOc5GHnD6Ohvo55kz6T/wfJl0dwbduCbMRsBEbeFKwWVoDXgT64YECA3rg+5YZRVjrro8jmJM8MvY161epsNnnOkV7vAttkjM0HPtYpMYwKoSqKbnriiin+DjcRXg8sEZE5fv1I4PHSiGcY0XS2PEiUApo8c0nbP2xQkYyeMpfGkHN3Jps850ivT9De3BngYmBy3pc3Koxqi/yL84HMxxVUvBPXy/wBXFXeHwH/LLpkhpGFzpYHiVI0Tc0toX6NYmSTx82i0rgWl0WeUh71uGysyXlf2qhAEn8fKoS4Yoo3llIQw8iViWP2DPWBJH2g71hfFzqjAELNYMXIJs86i3oZ2C1j4xvAwLwvaVQwFVd0MwtZfSAiMhS4DBiG84UAoKpWzt0oK519oE8csyfn37YodFvUP2yhs8mjlNhO29Z1bKRwJzCuYJeueKrJF1Aoor4PZSm6mYAkTvQ/4yyt04DDcD3KLJ/VqAg680AfN6KBS+5awrqNLR22leofNmwWNXX2RzllUSB542Tg7yURp2KoNl9AoejsrLrUJFEEdap6Py7k91VVnQwcW1yxDKM0XHzcPmWtkjtuRAOXnbQfDfV1HPLKh3jll8emK48P6HbKA6rPF1Aogt8HwYWI5xKWXmqSzEA+EJEewIsich4uhHfr4oplGKWhlFVyo0wy4wY3MO6ijOs9A+xTcBGqhmrzBRSSshTdzJMkCuQ7uFIm38a1ojkMOLOYQhmVR1e2R5fiHzbKJHPE8QPZemXg3/A3wPeKKkpVUG2+gO5KVhOWqj6hqu+q6kpVPUdVPwecku04EbleRN4UkWcCY5NFpFFEFvmfYwLbLhKRZSKyVETGBMbH+rFlIjIpj89odJLUw6/RJ9NVTAmPKiLTJPPlJ4bw3KVj25XHEFyWVQmURzHLvxSKimjAZWQlyQwkjFOB72fZ5wbg98BNGePTVPXXwQERGQacjpu07wjcJyJ7+M1X4ZIXVwJPiMhMVX02T7mNPKjKqrQVRsr0svtbW3PfdZ9O37gW6FcaOarFOV3WBlxGYvJVIJkBhh1Q1f+IyOCE5zsBuFVVPwCWi8gyXM4twDJVfRlARG71+5oCKSGFtEd3ZVNYHIO27stDPzksbeyMUx9j+Yj3mNevdHW0qulloJp8Ad2VuFIm/aM2kUCBxHCeiJyJy3S/QFXXAQ3Ao4F9VvoxgBUZ46Mi5J0ATAAYNGhQJ8QzMimUPbpS336LrtROhYf+3q48bvnoa1x09NOu8OOY/Qp3nQR0Z+e0UXjifCALaC9nEvyZD2zK83pX4/JqhwOrcC7DgqCq16jqSFUdOWDAgEKd1qBw9uhKDM0sqn/nn7hXrUAY7id/MZcfHv102cIzO1v+xTCCxJUyGVLoi6nq6tSyiPwJ+JdfbSS97c1OtFf8jRo3SkSh7NGV+PZbFJPOm3QsNbIM2A3+SxnKvgeotkQ1o7LJ1weSFyKyg6qu8qsn4qLdAWYCN4vI5Tgn+lBcxV8BhorIEJziOB34fCllNhyFsEdXYmhmQZWa4tquNQXG/og3rFYG5pw2CknRFIiI3AIcCmwvIitx5VAOFZHhuH+1V4CvAajqEhG5Hecc3wx8U1Vb/XnOA2bjWuper6pLiiWzUVyK9fbbGR9GwZTaJaRXxv04Fdv0wJzTRqHI2pGwGrGOhJVL0od9LvuFdQ3s17eWi4/bJ+uDstNdDZ+kYyOnDVitBqMqKVhHwpgoLABUdW0ughkGJHv7zSVaK8yHAbBuY0uiCK+8TTrNuPoMQR6mY4tZw+jCxJmwFuBMTQIMAtb55XrgNVzurGF0irCZRi6O7ThfRVJneM4mnSOB+wLrFwC/jtjXMLowWaOwfLTUnap6t18/mm7VlcAoFlEzjbAZBYQri7imUFHH5M3fgDMyxlqx5gZGtyXJV//AlPIAUNV/AwcXTySjuxA106iR8DzVMMd2WI5KtmNyZgVu7n1GxphiysPo1iT5+r8uIj8WkcH+50fA68UWzOj6RM0OWlUTJy6m+ifU19V22NbpCK8ttBtwU9yMUxw75X9aw+gqJFEg44EBuIaa0/3y+GIKZXQPomYHqSztpE11xo1oYNHFR/Hb04Z3qhFPsErtrYe85gLHUxyFUxz2zTeMNhKH8YrIVqr6XpHlKQgWxlsddDqEtgiy7LN8W/7xtwwL7UbAKn0Y3YCChfEGTngwcC0usn2QiOwPfE1Vv5G/mIZRWVnRV81cxnOXjk0bO/as/9K0Vwvz6spbfsQwKpUkmejTgDG4ciOo6mIR+VRRpTK6DRWRFT0S5ixo79Fx+SFLuXL0MgCkKeogwzASlTJR1RWSHhkTHmdpGNXEH4Fz21fX1m3igG/NSWtWYFVqDSOaJApkhTdjqYjU4nqkP1dcsQyjiLwE7J4+dPd9q7jgwcXQ0j5mVWoNI54kCuRc4Apcg6dG4F7A/B9GG1XTZXAzkBntOxM4Do5hBzb131Idn8MwKoQkCmRPVf1CcEBERgPziiOSUU1UapfBDnwZuD6wfipwW/oucf6YqlGShlFCkiiQ3wEHJBgzuiEV32P7XlwISJBNdJyJxFA1SrICMEXbvYirxnsQrmTJABH5XmDTtqSnWBndmErsMgjAWuBDGWPPAnvnfqqKV5IVgina7kdcJnovXO5HT2CbwM87wMnFF82oBiqyx/YQ0pXHNFwWeR7KAypYSVYYldjz3igucdV4HwIeEpEbVPXVEspkVBEV1WP718DEwPpQ4IXOn7YSW/FWIqZoux9JamFdKyL1qRUR6Scis4sok1FFpIoZdqYGVad5Bpe7EVQe6yiI8oDwir8W4tuRipyNGkUliRN9e1Vty8dV1XUi8uEiymRUGWXLJt8E9M4Yuw84vLCXSX22S+5awrqNLlGkd0+r455JRc1GjZKQRIFsEZFBqvoagIjsgrMoG0b5om4+h6sNnWICLrM8gkLI+X7LlrblpuZkLXO7E5VU28woDVmr8YrIWOAa4CGcoeCTwARVrVgzllXjLQ1lqaZ7J3BSxthmYuMCCyHn6ClzIzsfNtiD0ugiFLwar6reIyIHAAf6ofNV9a18BTSqj+Dbe33fWlRhfXMLPURozXgBKVp462rgI+lD9858g6OOax+MmmUUIgw3zhFs4apGdyUuD2QvVX3eKw9o70I4yJu0niy+eEa5yXx7T/kAgA7KI0XcwzZnU5IC2wEb2od+MPYpbt9/BXWP13DZTm4WEZeDUIjooGy91y0vxOiOxM1ALgC+CvwmZJsC1iShGxD29p6NqKibnBPNfgr8vH114Q7rOPHMR9rWgw/tuFlGIcJwwxzEmVi4qtHdiMsD+ar/fVjpxDFKSZLZQK4PxaiomxkLG7ng9sXJTF7zgY+nH7/Pd+/hvV4dH94p+eJmGdNOG97h4V/bQ9i4aTNDJs1KNBMKOoijZiIWrmp0N+JMWJmuyjRUdXrcdhG5Hvgs8Kaq7uvH+uNK2A0GXgFO9WHBgqv4ewyugejZKROZiJwF/Nif9lJVvTH7xzKykXQ2kM10A1AjwhbVDg/ilILKdnzbw78Z6Jux8RHgIKif0ov3Qs7TQ4QZCxtjZxmZ0UHb1dXy3nVY3noAAB00SURBVKbNbea4pD6MVLhylFPewlWN7kZcMPtx/ufLwHXAF/zPtcCXEpz7BmBsxtgk4H5VHQrc79cBjsblDQ/FBWReDW0K52JgFPAJ4GIR6Zfg2kYWspWdmLGwsS3ySMJO4BHgN6fuz/IpxzJv0mfSlMdF05/OqjzA2UPn7742XXn8wG84yK2GJfOB88NcNP1pDttrQGyy37gRDcyb9BmWTzmWrXr3pKU1fCaUhIpInjSMCiDOhHUOgIjcCwxT1VV+fQeccohFVf8jIoMzhk8ADvXLNwIPAhf68ZvUxRQ/KiL1/jqHAnNUda2/9hycUrolyYczookz+WS+YccFeit0mHG83tQcGqEVxonPNDBt1vC29ZYeWzj00geZOHZPxtH+QE5dI8oMNuupVVx20n6RJrmgbFFS5WKuq4hWvIZRZpIkEu6cUh6e1cCgPK83MHCuN4CBfrkBWBHYb6Ufixo3Osl2dbU0Nbd0GN+xvo5L7lqS2HHe4O3+mUonm/LY8Z0+PHJ1esr4qG/cx+ptPoD1hJqUxo1o4Lu3LQo9X8ocNW9Sx9iOMJNTqEzmwzCMnEiiQO73ta9Sb/2n4QpGdApVVREpWEa7iEzAmb8YNChf/Vb5FCKjesbCRt7btLnDeG0P4bC9BvDXR19LdJ6giShptJYoLP/VsWlj5x3/JP/ae1XaWFRYbJxPJiqMNols5sMwjNxJkkh4noicCHzKD12jqnfmeb3VIrKDqq7yJqo3/XgjsHNgv538WCPtJq/U+IMRcl6Dy5hn5MiRXbLUSi5hsHGKZurspR18AABb9+nJrKdWdRiPImj3T2L+ufi+YZyzYEjb+gO7vsk5pzwRuX/KnBb8HHEKLkqGOD+MgJXcMIw8STIDAXgS2KCq94lIXxHZRlU3ZD2qIzOBs4Ap/vc/A+PnicitOIf5eq9kZgP/F3CcHwVclMd1uwRRju9L7lrS4SF7x4LGSEUT9aBt2tiSuMhZQyC6CeJnBh9f0Y+/33xw2tieF/ybD3puCd0/Rd9eNXz3tkVtMjU2NXPHgkbqanvQ3NLx2DAT1IyFjQjhfpyG+rpQk5dhGMnIqkBE5Ks401B/YDecD+IPZKl5KiK34GYP24vISlw01RTgdhH5MvAqrjM1wN24EN5luDDecwBUda2I/BxIvab+LOVQ745EPfjXbWxJC0n926OvdXhgBk1CUQ/7KL9IGJnmnsP2GtDhuttv6cX8qUem7XfM2f/l2YHvJLrGe5s6mp2aW1rp17cWkERhtFNnLw1VHhLyGQzDyI0kM5Bv4kJoHwNQ1ReTlHNX1fERmzooHh999c2I81wPXJ9Azi5PkpwMiI6aSimgsKzq2h7CO+8nUx6QbjKbsbCROxY0pl33hw/sxYTHd2tbn/rJpVx18LLE54+jaWML004bnsgXFKV0g9FjhmHkRxIF8oGqbnK5fiAiPbFy7mUhSTmNOLarq2X0lLltRRF79+zB+uYWdqyvY2MgsS4bDRmmoqBp7ZPLt+cvt49q2/bfoWv44omPE5tMkiPqr5nEbxGldDM/g2EYuZNEgTwkIj8E6kTkSOAbwF3FFcsII6yxUVJqewjvbdrcZqJat7GFutoapp02nHEjGhgyaVai84SZil5vambAu7154qoj2saa+mzikHMf4N3eHaO9CkHS7HFrcmQYxSNJW7ULgTXA08DXcP6KH8ceYRSV90McyGGkMqXr62ppVY3Nvk6SA1Ej0jHjuhVuu+OgNOXx2bP+y/DvzAlVHlLAmUiS7HHLGjeM4hHbUEpEaoAlqrpX6UTqPF25oVRcY6MgqQijJEl0DVnCYyGiAdNVwHntq5ccvoQ/j3wlVi4BvnDgoMS5JtkQYPmUY8vXGdEwuhC5NpSKnYGoaiuwVES6bmZelZEk3yLXBL9UeGwcacpjEe7JnVIeh8GMJxq594jVbW/5LlKqIzvW13HpuP2orwvf3lBfx29PG96hrlXUxGXH+rq0ultKu3lrxsL4z2QYRudI4gPpBywRkceB91KDqnp80aQyOpB6w46aL0ZVxE1a3ynbDGXciAbX1Gk3nEEzhS8uM44Gxo1Mj8yK8z1MPn6fyO1hvbUHf6iOeS91jOA+bK8BBek4aBhG7iRRID8puhRGLNnMUHH9vZOG/kYhwMSj9oSv47J/UszCZe5EEKYEgsohs79GjUiaTyOzWOHoKXNDr/PA82sK0nHQMIzciesH0gc4F9gd50C/TlWLE1JjxBJnhmrIYu/vbOjvES8OZNzH2s/90vh32e3mrTvsl610yutNzWnKIbNXSKr4YmNTMxP/sbhtvxRxSqIQHQfNh2IYuRM3A7kRaAH+i+vXMQz4TimE6mp09uEU9fAUwqvPBkld57u3LyJBdfU2Mqvlrtx2I0d85SGkr3DZwv06JBKG1eia/+ra0JIqmeOZtLQql9y1JFGplNT97Eyobs6tdg3DAOKd6MNU9QxV/SNwMvDJEsnUpSiEgzfqTTrpG/a4EQ1s1yfcaZ1Jz1Zh+l8OTlMeR33pIQ75+gO8X7slNHQ2ygdxy2MrEo9nkpnnEtZQKugz6UyobrbmWoZhhBM3A2n7D1bVzVLIAP5uRCEcvGF1ppK8Yc9Y2JhT0uGEx3blhw/u3bY+acxT3Dp8RYf9MmdEUTOkqJ4gSRpNZZLEp5LvbMF8KIaRH3EKZH8RSVW9E1wm+jt+WVV126JL1wXo7MMprM6UAJ/7WPwDc8bCRib+Y3Fo2fZMhr9ez4y/jG5bn7P7aiacNB+NeGfInPlEmZdqIroSRo0HCQvzLVYXwEL4UAyjOxJpwlLVGlXd1v9so6o9A8umPBLSGfPTjIWNXHD74g4zGMVFH0UdM3rKXM6/bVFW5bHt+z15/jdj05THx86bw1c/F608wmY+Ueal8aN2TjwepLaHMPn4fWJlLyRx5jHDMKJJUsrE6AT5PpxSvpOoN/WwGUzQ3xKLwuX/2p+nrhhDn81OtvGnP8rgC2fx9lab6CG0+RLOOHBQWlJg754dvzJRPohLx+3HZSft1+H4kbv0T9u/vq6Wfn1r246desr+JXVeW7kTw8iPpA2ljDzJZruPIlsGedgMJknW+fHP7siVd41oW7/yoBe5/FMvpO2zbZ9aFl18FNBuQkvR1NySFqGUGWGWKs4YJFi7K3X8ZSftV1HNnIplHjOMrowpkDzJNTT3vQ82t0VhXXLXEiA+RDTOR1JbI6EzmLhjdlnXl4euOaxt/YUPbeCzZz/MppCugOsDTaWyRShlC3+1LHHD6LqYAsmDXHuTT/z7Ylq2tJui1m1sCU2WCxKbQR7h2gg7ptfmHsy8cTR7vdXutpoz4w0mLnqKTc3hVX2Ds5u4PuPn37aow3imcrAIJ8PoupgPJA9yyRuYOntpmvJI0dKqTJ29tM3pPWTSLEZPmduWHxLmO2k7douGXivzmPMfHsoLvzm6XXn8DVA48oSPMPn4fait6egpr+2RPrvJJxIpqBw6m8NiGEblYjOQPMjlrTruTTs1c4mbyYS95UedN3XMnKtXc9WfDmjf8Hngr6SVtJ06e2lolNbWfXqmzYryKYUSVA7W0Mkwui42A8mDXN6q4960UwUEg2QWFIxqvRp63jUw7oCGduXRC1iLm3lkTDaiFFtTIOkw5edpbmmlJmEiaaZysAgnw+i62AwkD3J5q544Zs8OPhBwjvCoPI3gwz3RtbYAnwNmBE7yCHBQ9GeIS54Ly2BvVaWutoY+tT0iM9ujCjsWI8LJih8aRvmxGUge5PJWPW5EA1NP2T8ts7pf31qmnrx/otlF1mtdD9TQrjwuwznZY5QHROenHLbXAC6a/nSokmhuaUWV0ON+e9pw5k36TEke4tZAyjAqg9iWttVKtbS0jWq6FGfiSb15911Ww5zrPt02vnbfTYw56SHWfLAJcMl5k4/fJ2u5k8y3+GCJ9TAEmHba8LK+/Ue19U218TUMIz9ybWlrJqwykmuS4YyFjVxy2xLuuvoQdnqnb9v4r//4PH989WVaPmh/GWhqbmHi3+NDhcNMS9+NcNqn2NF3JyynuchCgw2jMjAFUmYyH8apsN4whdL87VYWPnxU275fO3E+s/dYTc3y8OKEqXDfXB72cfknlRI9ZcUPDaMyMB9IjkTlbRTq3GG2/UeuegsExj88CICb93+VwT+Yxew9VgPx5dFzfSuPyj+pr6utmOgpK35oGJVBWWYgIvIKsAFoBTar6kgR6Q/cBgwGXgFOVdV14hqRXIHrwL0ROFtVnyyH3LlmoHe2/tWHN/Tm8f93RNt6U99NHDLhAd7tnd5ZOK48eq5v5fnW7iol1SCjYXQHymnCOkxV3wqsTwLuV9UpIjLJr1+Ia6c71P+MAq72v0tO0rpO+bZITc0WemyBv9w+itGvbt++8Ul4kDW0TtdAqy/35v25jzVw2xMrOoQFZ2aVJ6XcPo4kVIOMhtHVqSQT1gm4Puz43+MC4zep41GgXkR2KIeASZ23UYomVUQxih3r6zhrwWBennpsm/K4+IgljL5sLoyIL5s+9eT908qm19fVlrwsumEY3YtyzUAUuFdEFPijql4DDFTVVX77G8BAv9wABPuqrvRjqwJjiMgEYALAoEGDiiJ0UudtlKJZt7GlzWcyeeYSmnzV2359a7l86HDmXdQegvrwLm9x5qmPsaUHnLFX++cJe/NOmcuaNrbQr28tqq6ibjCjPbifmX0MwygE5VIgh6hqo4h8GJgjIs8HN6qqeuWSGK+ErgGXB1I4UdsJywqv6SG83tTM4EmzqBFh/KidYyOZLpr+FM2B/hhbfVDD/VceSv/mXm1jn/jGfby5zQdt67c8toKRu/QPfdhnmsuCCYBB05m7du5mNcMwjCjKokBUtdH/flNE7gQ+AawWkR1UdZU3Ub3pd28Edg4cvpMfKzmZztu+vWp4b1O7MmlV5a+Pvsbo3fpHKpA25aHwf7P35fOLd2nbdtYpj/PQrh1b1baqRj7sszWRCtbWsr4chmEUkpL7QERkKxHZJrUMHAU8A8wEzvK7nQX80y/PBM4Ux4HA+oCpq+SMG9HAvEmfYfmUY9M67QV59OV1aaVLMjnqhYG88qtj25THnz7+MoMvnBWqPFJElYtPEqb7elOzJd8ZhlFwyjEDGQjc6aJz6QncrKr3iMgTwO0i8mXgVeBUv//duBDeZbgw3nNKL3I4UaGzrapMPn6fDuauhvV1zPtDu59jxXYbOfLLD/F+bbgiyqSxqZkZCxvTZgyxjacC+6SOj9pmGIaRKyVXIKr6MrB/yPjbwOEh4wp8swSipZHE4RyVf1Ej0rbv5JlLePfdzfzjbwcxfFW/tn2O/NJDvDjg3ZzlyjRlZevXEUyws74chmEUEitlEkLSPI7xo3bmr4++1uH48aPaXTZf/M8uXDC3/SH9g7FPcfv+K9L2792zBy2bt5BkHpLpt8j0y9QHorDCFJ9FYRmGUSisGm8IuVR7/fGMp7nlsRW0qrZFYV06bj/4H3Bw+36zh77BuScuQMXNULaoJq6Am4kAy6ccm+enMwzDCMeq8RaAbA7nTPPWb04NJOytA3oDm9qPO+Bbc1jbt31gi2qaAshWATeTHiIdfCGGYRilxhRICFGO6R4iDJ40C8FlQkLAvKXwsR/1Y+d72susf+WsJ7jvI292OE+m43q7utq2pMIkxIX1GoZhlIpKKmVSMURVpE05zDONfkcuHsi4jzW0KY8rDn7BheU2rKG2R3ovcQEO22tA2/qMhY28tym9OGISosJ6DcMwSoXNQELIdEz3iIi2Gry2Lw/+6bC29ee3f4fjz5rHpp7OHd7SqvSt7cHmLdqmdBS4Y0FjW2b51NlLI3ujZ8NyOAzDKCemQCII1pwaMmlW2rZem3vwrxsOYY+3t2kb+/SEB3i138YO59kYkmwYjKTqjBKwHA7DMMqJKZAEBH0U3/vPHnz7f0Pbtn3ruCe5a1juifEpxZEkETAMgUQ5HEGH/3Z1tYhA08bwEF/DMIxcMAWSABE46NUPccutB7aNTd9nJd87drF7kudBavYwccyenB8ThRV02AfHvnDgoKwP/8x8lqCj3oopGobRWUyBZGMNLLy4vQ95c89WRn3zPt7pk7vjO0UwA3zciIa00u6ZZCqPfn1rufi4fRg3oiFrtnzSQoumQAzDyAeLwopiC66V1Yfbh8Z9cR57X3BPp5RHv74de4tPPn6f0KivMPr26tmmPML6pwd7tCcttGgYhpEPpkDCuBaowdUBBp759nr2/vE9LNqxqdOnDqvgG9ZpMIrUAz+uvW6KJE52c8QbhpEvpkAyEeCrfvkgYBPse8V2XHbSftRIng6PAFH5G8Ey8fMmfSZSiaQe+EnKs0fls6SwYoqGYXQG84EECZQf4VWY8XYjU3/T7mOIKt+eK0nMRmFVdoMP/CTtdTPzWSwKyzCMQmIKJEgv2rzWYRV5oxCBXHTLdjHNplJkPvwzH/jZFEzwPKYkDMMoBqZAQpixsJELbl+caMZRV1vDZSftFxuKm0lSS1jcwz+bgjEMwyg2pkAySM08kpqr8qlJ1bQxeeHEOGx2YRhGOTEFkkG23Ikwcs0k70zkU5JOiYZhGKXAFEgGxc6LCPopclUGSTslGoZhlAIL482gGHkRwfyOVBJhkkTATJLkfhiGYZQKm4FkkK02Va6EtcGFeGUQNZtIkvthGIZRKmwGEiBlUioUcYl6+SiDqNmRZZMbhlEOTIF4gialzpCK0A2aq8LIRxmEZZZbNrlhGOXCTFiefKKvMglWys1G0kTAIJb7YRhGJWEKxFMIP0JYocQo8lUGlvthGEalUDUKRETGAlfg6uReq6pTCnn+fDsDBsm1v4YpA8Mwqpmq8IGISA1wFXA0MAwYLyLDCnmNKP/CGQcOagvD7de3lvq62tgmhBYRZRhGd6FaZiCfAJap6ssAInIrrt3Ts4W6QK4mpdFT5mathmsYhtGVqRYF0gCsCKyvBEYFdxCRCcAEgEGDBuV1kVxMSvk4wQ3DMLoSVWHCSoKqXqOqI1V15IABA4p+vbAugnFhu4ZhGF2NapmBNAI7B9Z38mMlJax2VViWuWEYRnegWhTIE8BQERmCUxynA58v9kWDCmO7ulre27SZllZX5t0KGRqG0d2pChOWqm4GzgNmA88Bt6vqkmJeM7PYYVNzS5vySGGFDA3D6M5UywwEVb0buLtU10uamW5hu4ZhdFeqYgZSDpIqBgvbNQyju2IKJIIkisHCdg3D6M6YAokgLDO9tofQr2+the0ahmFQRT6QUmOVbw3DMOIxBRKDFTs0DMOIxkxYhmEYRl6YAjEMwzDywhSIYRiGkRemQAzDMIy8MAViGIZh5IWoava9qgwRWQO8mufh2wNvFVCcUlBtMlebvGAyl4pqk7na5IV4mXdR1cT9MLqkAukMIjJfVUeWW45cqDaZq01eMJlLRbXJXG3yQmFlNhOWYRiGkRemQAzDMIy8MAXSkWvKLUAeVJvM1SYvmMylotpkrjZ5oYAymw/EMAzDyAubgRiGYRh5YQrEMAzDyAtTIB4RGSsiS0VkmYhMKrc8QUTkFRF5WkQWich8P9ZfROaIyIv+dz8/LiJypf8cT4nIASWS8XoReVNEngmM5SyjiJzl939RRM4qg8yTRaTR3+tFInJMYNtFXualIjImMF6S746I7CwiD4jIsyKyRES+48cr9j7HyFzJ97mPiDwuIou9zJf48SEi8pi//m0i0suP9/bry/z2wdk+S4nkvUFElgfu8XA/Xrjvhap2+x+gBngJ2BXoBSwGhpVbroB8rwDbZ4z9CpjklycBv/TLxwD/BgQ4EHisRDJ+CjgAeCZfGYH+wMv+dz+/3K/EMk8Gvh+y7zD/vegNDPHfl5pSfneAHYAD/PI2wAteroq9zzEyV/J9FmBrv1wLPObv3+3A6X78D8DX/fI3gD/45dOB2+I+SwnlvQE4OWT/gn0vbAbi+ASwTFVfVtVNwK3ACWWWKRsnADf65RuBcYHxm9TxKFAvIjsUWxhV/Q+wtpMyjgHmqOpaVV0HzAHGlljmKE4AblXVD1R1ObAM970p2XdHVVep6pN+eQPwHNBABd/nGJmjqIT7rKr6rl+t9T8KfAb4hx/PvM+p+/8P4HARkZjPUip5oyjY98IUiKMBWBFYX0n8l7zUKHCviCwQkQl+bKCqrvLLbwAD/XIlfZZcZawU2c/zU/vrU+YgKkxmbyYZgXvbrIr7nCEzVPB9FpEaEVkEvIl7kL4ENKnq5pDrt8nmt68HPlRKmTPlVdXUPf6Fv8fTRKR3prwZcuUsrymQ6uAQVT0AOBr4poh8KrhR3fyzouOxq0FGz9XAbsBwYBXwm/KK0xER2Rq4AzhfVd8JbqvU+xwic0XfZ1VtVdXhwE64WcNeZRYplkx5RWRf4CKc3B/HmaUuLPR1TYE4GoGdA+s7+bGKQFUb/e83gTtxX+jVKdOU//2m372SPkuuMpZddlVd7f8ZtwB/ot3kUBEyi0gt7kH8N1Wd7ocr+j6HyVzp9zmFqjYBDwAH4Uw9qTbgweu3yea3bwe8XQ6ZA/KO9eZDVdUPgD9ThHtsCsTxBDDUR1n0wjnCZpZZJgBEZCsR2Sa1DBwFPIOTLxUlcRbwT788EzjTR1ocCKwPmDdKTa4yzgaOEpF+3qRxlB8rGRn+ohNx9zol8+k+4mYIMBR4nBJ+d7xd/TrgOVW9PLCpYu9zlMwVfp8HiEi9X64DjsT5bh4ATva7Zd7n1P0/GZjrZ4JRn6UU8j4feKkQnL8meI8L873I1/Pf1X5wkQkv4GydPyq3PAG5dsVFciwGlqRkw9lY7wdeBO4D+mt7RMZV/nM8DYwskZy34EwRLTjb6ZfzkRH4Es7ZuAw4pwwy/8XL9JT/R9shsP+PvMxLgaNL/d0BDsGZp54CFvmfYyr5PsfIXMn3+aPAQi/bM8BP/fiuOAWwDPg70NuP9/Hry/z2XbN9lhLJO9ff42eAv9IeqVWw74WVMjEMwzDywkxYhmEYRl6YAjEMwzDywhSIYRiGkRemQAzDMIy8MAViGIZh5IUpEKPbISLjRERFJGt2sYicLyJ9O3Gts0Xk90nHY87ziohs39nrGkYhMQVidEfGAw/739k4H8hbgRhGV8YUiNGt8DWZDsElDZ4eGK8RkV+LyDO++Ny3ROTbwI7AAyLygN/v3cAxJ4vIDX75OHG9IBaKyH0iMpA8EJGrRWS+BPo6BPiBuL4wj4vI7n7/ASJyh4g84X9Gh5zzFP+5FovIf/KRyzDC6Jl9F8PoUpwA3KOqL4jI2yLyMVVdAEwABgPDVXWziPRX1bUi8j3gMFV9K8t5HwYOVFUVka8APwAuyEO+H/nr1gD3i8hHVfUpv229qu4nImcCvwU+C1wBTFPVh0VkEK70xN4Z5/wpMEZVG1MlLwyjEJgCMbob43EPXXA9JcYDC4AjcE2BNgOoatI+ISl2Am7z9Yd6AcvzlO9UcSX7e+KaMQ3DlagAV3ol9XuaXz4CGObKHQGwrZ9lBZkH3CAitwPTMYwCYQrE6DaISH9cU6D9RERxXe5URCbmcJpg7Z8+geXfAZer6kwRORTXcS9X+YYA3wc+rqrrvHkseA0NWe6Bm/m8n3Gu9h1VzxWRUcCxwAI/63o7V/kMIxPzgRjdiZOBv6jqLqo6WFV3xs0UPolrGvQ18eW6vbIB2IBrxZpitYjsLSI9cFVkU2xHe+nrfHuMbwu8B6z3PpSjM7afFvj9P798L/Ct1A7i+14HEZHdVPUxVf0psIb0kt2GkTemQIzuxHhcP5Ugd/jxa4HXgKdEZDHweb/9GuCelBMd13P8X8AjuEq+KSYDfxeRBUA2f0mKs0VkZeoH10NiIfA8cDPO9BSkn4g8BXwH+K4f+zYw0jv+nwXODbnOVO98f8bLvTihfIYRi1XjNQzDMPLCZiCGYRhGXpgCMQzDMPLCFIhhGIaRF6ZADMMwjLwwBWIYhmHkhSkQwzAMIy9MgRiGYRh58f8BnaSaVEI/qgQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, r2_score\n",
    "\n",
    "# Use a Gradient Boosting algorithm\n",
    "alg = GradientBoostingRegressor()\n",
    "\n",
    "# Try these hyperparameter values\n",
    "params = {\n",
    " 'learning_rate': [0.1, 0.5, 1.0],\n",
    " 'n_estimators' : [50, 100, 150]\n",
    " }\n",
    "\n",
    "# Find the best hyperparameter combination to optimize the R2 metric\n",
    "score = make_scorer(r2_score)\n",
    "gridsearch = GridSearchCV(alg, params, scoring=score, cv=3, return_train_score=True)\n",
    "gridsearch.fit(X_train, y_train)\n",
    "print(\"Best parameter combination:\", gridsearch.best_params_, \"\\n\")\n",
    "\n",
    "# Get the best model\n",
    "model=gridsearch.best_estimator_\n",
    "print(model, \"\\n\")\n",
    "\n",
    "# Evaluate the model using the test data\n",
    "predictions = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: The use of random values in the Gradient Boosting algorithm results in slightly different metrics each time. In this case, the best model produced by hyperparameter tuning is unlikely to be significantly better than one trained with the default hyperparameter values; but it's still useful to know about the hyperparameter tuning technique!\n",
    "\n",
    "## Preprocess the Data\n",
    "\n",
    "We trained a model with data that was loaded straight from a source file, with only moderately successful results.\n",
    "\n",
    "In practice, it's common to perform some preprocessing of the data to make it easier for the algorithm to fit a model to it. There's a huge range of preprocessing transformations you can perform to get your data ready for modeling, but we'll limit ourselves to a few common techniques:\n",
    "\n",
    "### Scaling numeric features\n",
    "\n",
    "Normalizing numeric features so they're on the same scale prevents features with large values from producing coefficients that disproportionately affect the predictions. For example, suppose your data includes the following numeric features:\n",
    "\n",
    "| A |  B  |  C  |\n",
    "| - | --- | --- |\n",
    "| 3 | 480 | 65  |\n",
    "    \n",
    "Normalizing these features to the same scale may result in the following values (assuming A contains values from 0 to 10, B contains values from 0 to 1000, and C contains values from 0 to 100):\n",
    "\n",
    "|  A  |  B  |  C  |\n",
    "| --  | --- | --- |\n",
    "| 0.3 | 0.48| 0.65|\n",
    "\n",
    "There are multiple ways you can scale numeric data, such as calculating the minimum and maximum values for each column and assigning a proportional value between 0 and 1, or by using the mean and standard deviation of a normally distributed variable to maintain the same *spread* of values on a different scale.\n",
    "\n",
    "### Encoding categorical variables\n",
    "\n",
    "Machine learning models work best with numeric features rather than text values, so you generally need to convert categorical features into numeric representations.  For example, suppose your data includes the following categorical feature. \n",
    "\n",
    "| Size |\n",
    "| ---- |\n",
    "|  S   |\n",
    "|  M   |\n",
    "|  L   |\n",
    "\n",
    "You can apply *ordinal encoding* to substitute a unique integer value for each category, like this:\n",
    "\n",
    "| Size |\n",
    "| ---- |\n",
    "|  0   |\n",
    "|  1   |\n",
    "|  2   |\n",
    "\n",
    "Another common technique is to use *one hot encoding* to create individual binary (0 or 1) features for each possible category value. For example, you could use one-hot encoding to translate the possible categories into binary columns like this:\n",
    "\n",
    "|  Size_S  |  Size_M  |  Size_L  |\n",
    "| -------  | -------- | -------- |\n",
    "|    1     |     0    |    0     |\n",
    "|    0     |     1    |    0     |\n",
    "|    0     |     0    |    1     |\n",
    "\n",
    "To apply these preprocessing transformations to the bike rental, we'll make use of a Scikit-Learn feature named *pipelines*. These enable us to define a set of preprocessing steps that end with an algorithm. You can then fit the entire pipeline to the data, so that the model encapsulates all of the preprocessing steps as well as the regression algorithm. This is useful, because when we want to use the model to predict values from new data, we need to apply the same transformations (based on the same statistical distributions and category encodings used with the training data).\n",
    "\n",
    ">**Note**: The term *pipeline* is used extensively in machine learning, often to mean very different things! In this context, we're using it to refer to pipeline objects in Scikit-Learn, but you may see it used elsewhere to mean something else."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "gather": {
     "logged": 1667468558616
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('preprocessor',\n",
      "                 ColumnTransformer(n_jobs=None, remainder='drop',\n",
      "                                   sparse_threshold=0.3,\n",
      "                                   transformer_weights=None,\n",
      "                                   transformers=[('num',\n",
      "                                                  Pipeline(memory=None,\n",
      "                                                           steps=[('scaler',\n",
      "                                                                   StandardScaler(copy=True,\n",
      "                                                                                  with_mean=True,\n",
      "                                                                                  with_std=True))],\n",
      "                                                           verbose=False),\n",
      "                                                  [6, 7, 8, 9]),\n",
      "                                                 ('cat',\n",
      "                                                  Pipeline(memory=None,\n",
      "                                                           steps=[('onehot',\n",
      "                                                                   OneHotEncoder(categories='...\n",
      "                                           learning_rate=0.1, loss='ls',\n",
      "                                           max_depth=3, max_features=None,\n",
      "                                           max_leaf_nodes=None,\n",
      "                                           min_impurity_decrease=0.0,\n",
      "                                           min_impurity_split=None,\n",
      "                                           min_samples_leaf=1,\n",
      "                                           min_samples_split=2,\n",
      "                                           min_weight_fraction_leaf=0.0,\n",
      "                                           n_estimators=100,\n",
      "                                           n_iter_no_change=None,\n",
      "                                           presort='deprecated',\n",
      "                                           random_state=None, subsample=1.0,\n",
      "                                           tol=0.0001, validation_fraction=0.1,\n",
      "                                           verbose=0, warm_start=False))],\n",
      "         verbose=False)\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "\n",
    "# Define preprocessing for numeric columns (scale them)\n",
    "numeric_features = [6,7,8,9]\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('scaler', StandardScaler())])\n",
    "\n",
    "# Define preprocessing for categorical features (encode them)\n",
    "categorical_features = [0,1,2,3,4,5]\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
    "\n",
    "# Combine preprocessing steps\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)])\n",
    "\n",
    "# Create preprocessing and training pipeline\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('regressor', GradientBoostingRegressor())])\n",
    "\n",
    "\n",
    "# fit the pipeline to train a linear regression model on the training set\n",
    "model = pipeline.fit(X_train, (y_train))\n",
    "print (model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, the model is trained, including the preprocessing steps. Let's see how it performs with the validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "gather": {
     "logged": 1667468559145
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 105952.85711394435\n",
      "RMSE: 325.50400475868855\n",
      "R2: 0.792286073615376\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get predictions\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Display metrics\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pipeline is composed of the transformations and the algorithm used to train the model. To try an alternative algorithm you can just change that step to a different kind of estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "gather": {
     "logged": 1667468561193
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('preprocessor',\n",
      "                 ColumnTransformer(n_jobs=None, remainder='drop',\n",
      "                                   sparse_threshold=0.3,\n",
      "                                   transformer_weights=None,\n",
      "                                   transformers=[('num',\n",
      "                                                  Pipeline(memory=None,\n",
      "                                                           steps=[('scaler',\n",
      "                                                                   StandardScaler(copy=True,\n",
      "                                                                                  with_mean=True,\n",
      "                                                                                  with_std=True))],\n",
      "                                                           verbose=False),\n",
      "                                                  [6, 7, 8, 9]),\n",
      "                                                 ('cat',\n",
      "                                                  Pipeline(memory=None,\n",
      "                                                           steps=[('onehot',\n",
      "                                                                   OneHotEncoder(categories='...\n",
      "                 RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
      "                                       criterion='mse', max_depth=None,\n",
      "                                       max_features='auto', max_leaf_nodes=None,\n",
      "                                       max_samples=None,\n",
      "                                       min_impurity_decrease=0.0,\n",
      "                                       min_impurity_split=None,\n",
      "                                       min_samples_leaf=1, min_samples_split=2,\n",
      "                                       min_weight_fraction_leaf=0.0,\n",
      "                                       n_estimators=100, n_jobs=None,\n",
      "                                       oob_score=False, random_state=None,\n",
      "                                       verbose=0, warm_start=False))],\n",
      "         verbose=False) \n",
      "\n",
      "MSE: 103297.58730090907\n",
      "RMSE: 321.3994201937973\n",
      "R2: 0.7974915634294258\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a different estimator in the pipeline\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('regressor', RandomForestRegressor())])\n",
    "\n",
    "\n",
    "# fit the pipeline to train a linear regression model on the training set\n",
    "model = pipeline.fit(X_train, (y_train))\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Get predictions\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Display metrics\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(\"R2:\", r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions - Preprocessed')\n",
    "z = np.polyfit(y_test, predictions, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_test,p(y_test), color='magenta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've now seen a number of common techniques used to train predictive models for regression. In a real project, you'd likely try a few more algorithms, hyperparameters, and preprocessing transformations; but by now you should have got the general idea. Let's explore how you can use the trained model with new data.\n",
    "\n",
    "### Use the Trained Model\n",
    "\n",
    "First, let's save the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "gather": {
     "logged": 1667468561606
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/bike-share.pkl']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Save the model as a pickle file\n",
    "filename = './models/bike-share.pkl'\n",
    "joblib.dump(model, filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can load it whenever we need it, and use it to predict labels for new data. This is often called *scoring* or *inferencing*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "gather": {
     "logged": 1667468561896
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New sample: [1.0, 1.0, 0.0, 3.0, 1.0, 1.0, 0.226957, 0.22927, 0.436957, 0.1869]\n",
      "Prediction: 105 rentals\n"
     ]
    }
   ],
   "source": [
    "# Load the model from the file\n",
    "loaded_model = joblib.load(filename)\n",
    "\n",
    "# Create a numpy array containing a new observation (for example tomorrow's seasonal and weather forecast information)\n",
    "X_new = np.array([[1,1,0,3,1,1,0.226957,0.22927,0.436957,0.1869]]).astype('float64')\n",
    "print ('New sample: {}'.format(list(X_new[0])))\n",
    "\n",
    "# Use the model to predict tomorrow's rentals\n",
    "result = loaded_model.predict(X_new)\n",
    "print('Prediction: {:.0f} rentals'.format(np.round(result[0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model's **predict** method accepts an array of observations, so you can use it to generate multiple predictions as a batch. For example, suppose you have a weather forecast for the next five days; you could use the model to predict bike rentals for each day based on the expected weather conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "gather": {
     "logged": 1667468562196
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-day rental predictions:\n",
      "584.0\n",
      "780.0\n",
      "242.0\n",
      "204.0\n",
      "266.0\n"
     ]
    }
   ],
   "source": [
    "# An array of features based on five-day weather forecast\n",
    "X_new = np.array([[0,1,1,0,0,1,0.344167,0.363625,0.805833,0.160446],\n",
    "                  [0,1,0,1,0,1,0.363478,0.353739,0.696087,0.248539],\n",
    "                  [0,1,0,2,0,1,0.196364,0.189405,0.437273,0.248309],\n",
    "                  [0,1,0,3,0,1,0.2,0.212122,0.590435,0.160296],\n",
    "                  [0,1,0,4,0,1,0.226957,0.22927,0.436957,0.1869]])\n",
    "\n",
    "# Use the model to predict rentals\n",
    "results = loaded_model.predict(X_new)\n",
    "print('5-day rental predictions:')\n",
    "for prediction in results:\n",
    "    print(np.round(prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading\n",
    "\n",
    "To learn more about Scikit-Learn, see the [Scikit-Learn documentation](https://scikit-learn.org/stable/user_guide.html).\n",
    "\n",
    "## Challenge: Predict Real Estate Prices\n",
    "\n",
    "Think you're ready to create your own regression model? Try the challenge of predicting real estate property prices in the [/challenges/02 - Real Estate Regression Challenge.ipynb](./challenges/02%20-%20Real%20Estate%20Regression%20Challenge.ipynb) notebook!\n",
    "\n",
    "> **Note**: The time to complete this optional challenge is not included in the estimated time for this exercise - you can spend as little or as much time on it as you like!"
   ]
  }
 ],
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  "kernel_info": {
   "name": "python38-azureml"
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  "kernelspec": {
   "display_name": "Python 3.8 - AzureML",
   "language": "python",
   "name": "python38-azureml"
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  "language_info": {
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