ML-1-class-metrics.ipynb

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      "cell_type": "markdown",
      "source": [
        "© Валерий Студенников, курс \"Инструменты анализа данных\""
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
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      "cell_type": "markdown",
      "source": [
        "# Задачи классификации. Метрики качества\n",
        "\n",
        "Материалы:\n",
        "* книжка Гвидо, Мюллер: «Введение в машинное обучение»\n",
        "* [Открытый курс машинного обучения](https://habr.com/company/ods/blog/322534/)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Датасет с параметрами сортов ирисов\n",
        "![iris-structure](https://docplayer.ru/docs-images/67/57767148/images/28-0.jpg)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn, sklearn.model_selection, sklearn.neighbors, sklearn.svm"
      ],
      "outputs": [],
      "execution_count": 53,
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    {
      "cell_type": "code",
      "source": [
        "%pylab inline\n",
        "#import matplotlib.pyplot as plt\n",
        "import matplotlib.pylab as plt"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ],
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    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris_dataset = load_iris()\n",
        "\n",
        "list( iris_dataset.keys() )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
            "text/plain": [
              "['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename']"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 39,
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    {
      "cell_type": "code",
      "source": [
        "# Значение ключа DESCR – это краткое описание набора данных\n",
        "print(iris_dataset['DESCR'])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 150 (50 in each of three classes)\n",
            "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            "        - sepal length in cm\n",
            "        - sepal width in cm\n",
            "        - petal length in cm\n",
            "        - petal width in cm\n",
            "        - class:\n",
            "                - Iris-Setosa\n",
            "                - Iris-Versicolour\n",
            "                - Iris-Virginica\n",
            "                \n",
            "    :Summary Statistics:\n",
            "\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "                    Min  Max   Mean    SD   Class Correlation\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
            "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
            "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
            "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: 33.3% for each of 3 classes.\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
            "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
            "Machine Learning Repository, which has two wrong data points.\n",
            "\n",
            "This is perhaps the best known database to be found in the\n",
            "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
            "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
            "data set contains 3 classes of 50 instances each, where each class refers to a\n",
            "type of iris plant.  One class is linearly separable from the other 2; the\n",
            "latter are NOT linearly separable from each other.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
            "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
            "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
            "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
            "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
            "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
            "     Structure and Classification Rule for Recognition in Partially Exposed\n",
            "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
            "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
            "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
            "     on Information Theory, May 1972, 431-433.\n",
            "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
            "     conceptual clustering system finds 3 classes in the data.\n",
            "   - Many, many more ...\n"
          ]
        }
      ],
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        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Значение ключа target_names – массив меток классов (в данном случае сорта цветов)\n",
        "list( iris_dataset['target_names'] )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": [
              "['setosa', 'versicolor', 'virginica']"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
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        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Список названий полей-признаков\n",
        "list( iris_dataset['feature_names'] )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": [
              "['sepal length (cm)',\n",
              " 'sepal width (cm)',\n",
              " 'petal length (cm)',\n",
              " 'petal width (cm)']"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
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        "outputHidden": false,
        "execution": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Сами признаки записаны в numpy-массиве data\n",
        "type(iris_dataset['data']), iris_dataset['data'].shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": [
              "(numpy.ndarray, (150, 4))"
            ]
          },
          "metadata": {}
        }
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# Первые пять строк массива data\n",
        "iris_dataset['data'][:5]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": [
              "array([[5.1, 3.5, 1.4, 0.2],\n",
              "       [4.9, 3. , 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.3, 0.2],\n",
              "       [4.6, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.6, 1.4, 0.2]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# \"Ответы\" записаны в target\n",
        "type(iris_dataset['target']), iris_dataset['target'].shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": [
              "(numpy.ndarray, (150,))"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
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        "outputHidden": false,
        "execution": {
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    },
    {
      "cell_type": "code",
      "source": [
        "iris_dataset['target']\n",
        "# Значения чисел задаются массивом target_names: 0 – setosa, 1 – versicolor, а 2 – virginica."
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {}
        }
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    {
      "cell_type": "markdown",
      "source": [
        "## Метрики эффективности, обучающий и тестовый наборы\n",
        "\n",
        "Прежде, чем мы применять ML-модель в реальной жизни, мы должны убедиться в том, что модель на самом деле работает и ее прогнозам можно доверять.\n",
        "\n",
        "Разобьём весь датасет на две части. Одна часть данных используется для построения модели машинного обучения и называется обучающими *данными* (*training data*) или *обучающим набором* (*training set*). Остальные данные будут использованы для оценки качества модели, их называют *тестовыми данными* (*test data*), *тестовым набором* (*test set*) или *контрольным набором* (*hold-out set*).\n",
        "\n",
        "![train-test-set](http://www.dataminingapps.com/wp-content/uploads/2015/12/1-289x300.png)\n",
        "\n",
        "Обычно отбирают в обучающий набор 75% строк данных и оставшиеся 25% объявляются тестовым набором (либо 70% / 30%). \n",
        "\n",
        "В библиотеке `scikit-learn` есть функция `train_test_split`, которая перемешивает набор данных и разбивает его на две части."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "В `sklearn` данные, как правило, обозначаются заглавной $ X $ (т.к. это двемерная матрица), тогда как метки обозначаются строчной $ у $ (т.к. это одномерный вектор).\n",
        "Это навеяно стандартной математической формулой $ f(х)=у $."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n",
        "    iris_dataset['data'], iris_dataset['target'], random_state = 0\n",
        ")"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
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          "shell.execute_reply": "2020-02-28T13:26:25.460Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"форма массива X_train: {}\".format(X_train.shape))\n",
        "print(\"форма массива y_train: {}\".format(y_train.shape))\n",
        "print(\"форма массива X_test: {}\".format(X_test.shape))\n",
        "print(\"форма массива y_test: {}\".format(y_test.shape))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "форма массива X_train: (112, 4)\n",
            "форма массива y_train: (112,)\n",
            "форма массива X_test: (38, 4)\n",
            "форма массива y_test: (38,)\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
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        "outputHidden": false,
        "execution": {
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          "shell.execute_reply": "2020-02-28T13:26:28.946Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# создаем dataframe из данных в массиве X_train\n",
        "# маркируем столбцы, используя строки в iris_dataset.feature_names\n",
        "iris_dataframe = pd.DataFrame( X_train, columns = iris_dataset.feature_names )\n",
        "# создаем матрицу рассеяния из dataframe, цвет точек задаем с помощью y_train\n",
        "%pylab inline\n",
        "g = pd.plotting.scatter_matrix(\n",
        "    iris_dataframe, c = y_train, figsize = (12, 12),\n",
        "    marker = 'o', hist_kwds = { 'bins' : 20 }, s = 60, alpha = .8\n",
        ")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 16 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 17,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:26:42.865Z",
          "iopub.execute_input": "2020-02-28T13:26:42.877Z",
          "iopub.status.idle": "2020-02-28T13:26:44.401Z",
          "shell.execute_reply": "2020-02-28T13:26:44.438Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Классификатор kNN — \"метод k ближайших соседей\"\n",
        "\n",
        "Построение этой модели заключается лишь в запоминании обучающего набора. Для того, чтобы сделать прогноз для новой точки данных, алгоритм находит точку в обучающем наборе, которая находится ближе всего к новой точке.\n",
        "\n",
        "![knn-pic](http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1531424125/KNN_final1_ibdm8a.png)\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Создание объекта алгоритма/модели-предсказателя\n",
        "\n",
        "В `sklearn` все модели машинного обучения реализованы в собственных классах, называемых классами Estimator. Алгоритм классификации на основе метода k ближайших соседей реализован в классификаторе `KNeighborsClassifier` модуля `sklearn.neighbors`. Прежде чем использовать эту модель, нам нужно создать объект-экземпляр класса, указав параметры модели. Самым важным параметром `KNeighborsClassifier` является количество соседей, которые мы установим равным 1."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from  import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier( n_neighbors = 1 )"
      ],
      "outputs": [],
      "execution_count": 62,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# мне больше нравится не засорять namespace:\n",
        "import sklearn.neighbors\n",
        "knn = sklearn.neighbors.KNeighborsClassifier( n_neighbors = 1 )\n",
        "knn"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
              "           weights='uniform')"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Объект `knn` (как и любой объект с ML-моделью в `sklearn`) включает в себя:\n",
        "* алгоритм, который будет использоваться для построения модели,\n",
        "* а также алгоритм, который сгенерирует прогнозы для новых точек данных\n",
        "* информацию, которую алгоритм извлек из обучающих данных (параметры модели);<br> в случае с KNeighborsClassifier он будет просто хранить обучающий набор."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Построение модели"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Для построения модели на обучающем наборе, мы вызываем метод fit объекта knn\n",
        "knn.fit( X_train, y_train )\n",
        "# Метод fit возвращает сам объект knn (и изменяет его)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
              "           weights='uniform')"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Получение прогнозов\n",
        "\n",
        "Теперь мы можем получить прогнозы, применив эту модель к новым данным, по которым мы еще не знаем правильные метки."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = np.array( [ [5, 2.9, 1, 0.2] ])\n",
        "\n",
        "prediction = knn.predict( X_new )\n",
        "\n",
        "print( \"Прогноз:\", prediction )\n",
        "print( \"Спрогнозированная метка:\", iris_dataset['target_names'][prediction] )"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Прогноз: [0]\n",
            "Спрогнозированная метка: ['setosa']\n"
          ]
        }
      ],
      "execution_count": 18,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####  Оценка качества модели\n",
        "\n",
        "Это тот самый момент, когда нам понадобится созданный ранее тестовый набор. Эти данные не использовались для построения модели, но мы знаем правильные сорта для каждого ириса в тестовом наборе."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict( X_test )\n",
        "print(\"Прогнозы для тестового набора:\", y_pred, sep = '\\n')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Прогнозы для тестового набора:\n",
            "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
            " 2]\n"
          ]
        }
      ],
      "execution_count": 19,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Правильность на тестовом наборе: {:.2f}\".format( np.mean( y_pred == y_test )) )"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Правильность на тестовом наборе: 0.97\n"
          ]
        }
      ],
      "execution_count": 85,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred == y_test"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": [
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True, False])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Кроме того, мы можем использовать метод score объекта knn, который вычисляет правильность модели\n",
        "knn.score( X_test, y_test )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": [
              "0.9736842105263158"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# пример вычисление метрик с использованием sklearn.metrics\n",
        "import sklearn.metrics\n",
        "\n",
        "cl1 = [0, 0, 1, 1, 0]\n",
        "cl2 = [1, 0, 1, 1, 1]\n",
        "\n",
        "sklearn.metrics.accuracy_score( cl1, cl2 )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": [
              "0.6"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 22,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# рекомендуемый способ вычисления метрик\n",
        "sklearn.metrics.accuracy_score( y_pred, y_test )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": [
              "0.9736842105263158"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 23,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Обобщающая способность, переобучение и недообучение\n",
        "\n",
        "Построение модели, которая слишком сложна для имеющегося у нас объема информации, называется *переобучением* (*overfitting*). Переобучение происходит, когда ваша модель слишком точно подстраивается под особенности обучающего набора и вы получаете модель, которая хорошо работает на обучающем наборе, но не умеет обобщать результат на новые данные.\n",
        "\n",
        "Если модель слишком проста и из данных извлечена не вся доступная информация, модель будет плохо работать даже на обучающем наборе. Выбор слишком простой модели называется *недообучением* (*underfitting*)."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Переобученность / недообученность для классификации:\n",
        "\n",
        "![overfit-underfit-class-1](https://s3.amazonaws.com/geekbrains-uploads/geekbrains/public/ckeditor_assets/pictures/4334/content_1-wo81biq10n4jYYHPyWCKNg.png)\n",
        "\n",
        "![overfit-underfit-class-2](https://cdn-images-1.medium.com/max/1600/1*JZbxrdzabrT33Yl-LrmShw.png)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Переобученность / недообученность для регрессии:\n",
        "\n",
        "![overfit-underfit-regression-1](http://scikit-learn.org/stable/_images/sphx_glr_plot_underfitting_overfitting_001.png)\n",
        "\n",
        "![overfit-underfit-regression-2](https://cdn-images-1.medium.com/max/1600/1*_7OPgojau8hkiPUiHoGK_w.png)\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Чем сложнее модель, тем лучше она будет работать на обучающих данных. Однако, если наша модель становится слишком сложной, мы начинаем уделять слишком много внимания каждой отдельной точке данных в нашем обучающем наборе, и эта модель не будет хорошо обобщать результат на новые данные.\n",
        "\n",
        "Существует оптимальная точка, которая позволяет получить наилучшую обобщающую способность. Собственно это и есть модель, которую нам нужно найти.\n",
        "Компромисс между переобучением и недообучением показан на рисунке.\n",
        "\n",
        "![fitting-accuracy](https://bitbucket.org/despairr/ds-course-2018/raw/dbbafa803be8b1f01cc078ba3ba96d176b220669/intro-to-ml-images/206.png)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Взаимосвязь между сложностью модели и размером набора данных\n",
        "\n",
        "Чем больше семплов в вашем наборе, тем более сложную модель можно использовать, не беспокоясь о переобучении. Обычно больший объем данных дает большее разнообразие и позволяют строить более сложные модели."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### kNN и переобучение / недообучение"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Набор данных forge:\n",
        "![forge-dataset](https://bitbucket.org/despairr/ds-course-2018/raw/443959e3b5e41168ceba4adcffbe1e9e9084f246/intro-to-ml-images/219.png)"
      ],
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "knn_classification: n_neighbors = 1\n",
        "![knn_classification: n_neighbors = 1](https://bitbucket.org/despairr/ds-course-2018/raw/443959e3b5e41168ceba4adcffbe1e9e9084f246/intro-to-ml-images/238.png)"
      ],
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "knn_classification: n_neighbors = 3\n",
        "![knn_classification: n_neighbors = 1](https://bitbucket.org/despairr/ds-course-2018/raw/443959e3b5e41168ceba4adcffbe1e9e9084f246/intro-to-ml-images/239.png)"
      ],
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import mglearn\n",
        "\n",
        "# генерируем набор данных\n",
        "X, y = mglearn.datasets.make_forge()\n",
        "\n",
        "# мы разделим наши данные на обучающий и тестовый наборы\n",
        "import sklearn.model_selection\n",
        "\n",
        "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( X, y, random_state = 0 )\n",
        "\n",
        "X.shape, X_train.shape, X_test.shape, y.shape, y_train.shape, y_test.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": [
              "((26, 2), (19, 2), (7, 2), (26,), (19,), (7,))"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 24,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# создаём классификатор\n",
        "import sklearn.neighbors\n",
        "\n",
        "clf = sklearn.neighbors.KNeighborsClassifier( n_neighbors = 3 )\n",
        "clf"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
              "           weights='uniform')"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 25,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# подгоняем классификатор, используя обучающий набор\n",
        "clf.fit( X_train, y_train )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
              "           weights='uniform')"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 26,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Прогнозы на тестовом наборе\n",
        "y_pred = clf.predict( X_test )\n",
        "y_pred"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 1, 0, 0])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 27,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Правильность на тестовом наборе\n",
        "clf.score( X_test, y_test )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": [
              "0.8571428571428571"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 28,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Границы принятия решений, созданные моделью kNN для различных n_neighbors:\n",
        "\n",
        "![knn_plot_2d_separator](https://bitbucket.org/despairr/ds-course-2018/raw/443959e3b5e41168ceba4adcffbe1e9e9084f246/intro-to-ml-images/244.png)"
      ],
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Использование небольшого числа соседей соответствует высокой сложности модели, а использование большого количества соседей соответствует низкой сложности модели."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn.datasets\n",
        "\n",
        "cancer = sklearn.datasets.load_breast_cancer()\n",
        "cancer['feature_names'], cancer['target_names']"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": [
              "(array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "        'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "        'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "        'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "        'smoothness error', 'compactness error', 'concavity error',\n",
              "        'concave points error', 'symmetry error',\n",
              "        'fractal dimension error', 'worst radius', 'worst texture',\n",
              "        'worst perimeter', 'worst area', 'worst smoothness',\n",
              "        'worst compactness', 'worst concavity', 'worst concave points',\n",
              "        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),\n",
              " array(['malignant', 'benign'], dtype='<U9'))"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 18,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:28:04.025Z",
          "iopub.execute_input": "2020-02-28T13:28:04.035Z",
          "iopub.status.idle": "2020-02-28T13:28:04.057Z",
          "shell.execute_reply": "2020-02-28T13:28:04.066Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cancer.data.shape, cancer.data[0]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": [
              "((569, 30),\n",
              " array([1.799e+01, 1.038e+01, 1.228e+02, 1.001e+03, 1.184e-01, 2.776e-01,\n",
              "        3.001e-01, 1.471e-01, 2.419e-01, 7.871e-02, 1.095e+00, 9.053e-01,\n",
              "        8.589e+00, 1.534e+02, 6.399e-03, 4.904e-02, 5.373e-02, 1.587e-02,\n",
              "        3.003e-02, 6.193e-03, 2.538e+01, 1.733e+01, 1.846e+02, 2.019e+03,\n",
              "        1.622e-01, 6.656e-01, 7.119e-01, 2.654e-01, 4.601e-01, 1.189e-01]))"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 19,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:28:07.191Z",
          "iopub.execute_input": "2020-02-28T13:28:07.201Z",
          "iopub.status.idle": "2020-02-28T13:28:07.221Z",
          "shell.execute_reply": "2020-02-28T13:28:07.231Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Построим график \"Правильности\" для разного числа соседей\n",
        "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n",
        "    cancer.data, cancer.target, stratify = cancer.target\n",
        ")\n",
        "\n",
        "training_accuracy = []\n",
        "test_accuracy = []\n",
        "# пробуем n_neighbors от 1 до 10\n",
        "neighbors_settings = range(1, 11)\n",
        "for n_neighbors in neighbors_settings:\n",
        "  # строим модель\n",
        "  clf = sklearn.neighbors.KNeighborsClassifier( n_neighbors = n_neighbors )\n",
        "  clf.fit( X_train, y_train )\n",
        "  # записываем правильность на обучающем наборе\n",
        "  training_accuracy.append( clf.score(X_train, y_train) )\n",
        "  # записываем правильность на тестовом наборе\n",
        "  test_accuracy.append( clf.score(X_test, y_test) )\n",
        "\n",
        "plt.plot( neighbors_settings, training_accuracy, label=\"правильность на обучающем наборе\" )\n",
        "plt.plot( neighbors_settings, test_accuracy, label=\"правильность на тестовом наборе\" )\n",
        "plt.ylabel(\"Правильность\")\n",
        "plt.xlabel(\"количество соседей\")\n",
        "g = plt.legend()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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TE0NSUhJ79uyhX79+dOnShf79+7Nv377iz61ly5ZERUXRpUsXNm7cCFilNnr27EmXLl0YNGgQR48eLX6PNbV0d0VoC+ICmjcIoF/7RsxetZ+J/dvi6635tELm/RnSNrj2mE2iYeCLF9xEy31funLfjzzyCI888sg5d/pu2bKlzDLUAwcO5N5772XJkiW0bNmSzMxMateuXRxzZGQkCxcuLK5We9NNNzFixAhGjBjBtGnTmDhxInPnzgV+L3I4fvx4FixYQFRUFMOHD2fy5Mn06dOHv/71rzz77LPFybWmlu6uCE0QFzGsZzg/TU9i/uZDXB8ddvEdVJWg5b5dW+77fH766acyy1AvX76cK6+8srjs9cXiX7ZsGZ9//jkAd999N3/605+K1z3++OM8+eSTnD59mhUrVnD8+HGOHTtWXBRwxIgRDBkypHj7mlq6uyI0QVxEn3aNaFa/Nokr9mqCqKiL/NK3g5b7vjTlvs9Xhvrrr7+u5Lv5XdHn9t577/H000/z8ssvX3D7mlq6uyK0z+QivL2EOxPC+SX5CLsysu0OR7mIlvt2bbnv8zlfGeqePXuyZMmS4qqmF4v/8ssvZ/bs2YCVsHr37n3ONkVdYPXq1aNBgwYsXboUOLvEeJGaWLq7IrQF4YQhcc15ff52Plqxj/+9sZPd4SgX0HLfri33fT6dOnUqswx1z549mTp1KoMHD6awsLB4/Od8Jk+ezKhRo3j55ZeLpygtUtQ1VzQmANYX93333cepU6do1arVWdtDzSzdXRFarM9JD370G78kH2b5k/3x9/V223mqC08u1qflvpUnqGzp7orQYn1uMiwhnGOn8vh2fardoSil1CWhXUxOuqxVMK1CA0lcsZdbuze3OxxVCRfrilHqUhg5cmTxvRWeSlsQThIRhiVE8Nu+Y2w+mHXxHRTVpftSqeqgIn+PmiDK4dZuzfDz8dL6TE7w9/fnyJEjmiSU8gDGGI4cOXLWpdvO0C6mcqgfUIubujZl7poDPHl9R4L89OM7n+bNm5OSkkJGhlbDVcoT+Pv707x5+brH9RuunIYlhDNndQpfrj3AsIRLf9lZVeHr61t8h6lSqmrSLqZyimlRn05hdZm5fJ92nyilqjVNEOUkItzVM4ItqVms2X/M7nCUUsptNEFUwC0xTQny82Hmch2sVkpVX5ogKiDQz4dBsc34Zn0qx045X9NGKaWqEk0QFXRnQjhn8guZszrF7lCUUsotNEFUUMewunSPaMBHK3SwWilVPWmCqIS7eoaz6/BJlu08YncoSinlcpogKmFgVBj1A3xJXKFzViulqh9NEJXg7+vNkO7N+WFTGulZzs2wpZRSVYUmiEq6MyGC/ELDJ0n77Q5FKaVcShNEJbUMCaRXmxBmrdxPQaEOViulqg9NEC5wV89wDhzLYdG2dLtDUUopl9EE4QL9OzamUR0/HaxWSlUrmiBcwNfbi6E9WrBwWzr7M0/ZHY5SSrmEWxOEiAwQkW0ikiwify5jfYSI/CQi60VkkYg0L7HuJRHZJCJbRORNERF3xlpZQ+PDEWD2Km1FKKWqB7clCBHxBqYAA4FOwB0i0qnUZq8AHxpjugDPAS849r0cuALoAkQBPYA+7orVFZrWr02/Do35eFUKZ/IL7Q5HKaUqzZ0tiHgg2RizyxhzBpgN3FJqm07AAsfzhSXWG8AfqAX4Ab7AITfG6hJ39QzncPZp/rs5ze5QlFKq0tyZIJoBJW8OSHEsK2kdMNjxfBBQR0SCjTHLsBJGquPxgzFmS+kTiMhYEUkSkSRPmNryyrahNG9Qm8Tl2s2klKr67B6kfgzoIyJrsLqQDgAFItIG6Ag0x0oq/USkd+mdjTFTjTFxxpi40NDQSxl3mby8hDsTwlm26wjJ6dl2h6OUUpXizgRxAGhR4nVzx7JixpiDxpjBxphY4CnHsmNYrYnlxphsY0w2MA+4zI2xuswf41rg6y18pJe8KqWqOHcmiFVAWxFpKSK1gKHAVyU3EJEQESmK4UlgmuP5PqyWhY+I+GK1Ls7pYvJEIUF+DIgKY87q/eTmFdgdjlJKVZjbEoQxJh8YD/yA9eX+iTFmk4g8JyI3OzbrC2wTke1AY+BvjuVzgJ3ABqxxinXGmK/dFaurDUsIJys3n6/XHbQ7FKWUqjCpLpPdxMXFmaSkJLvDAMAYwzWvLyHIz4e5D15hdzhKKXVeIrLaGBNX1jq7B6mrJRFhWEI4a/cfY+OB43aHo5RSFaIJwk0Gd2uOv6+X1mdSSlVZmiDcpF5tX27u2pQv1x7gRG6e3eEopVS5aYJwo2EJEZw6U8DcNQcuvrFSSnkYTRBu1LVFfaKb1SNxxT6qy8UASqmaQxOEmw1LCGdr2gl+23fU7lCUUqpcNEG42c0xTanj58NMrc+klKpiNEG4WUAtHwZ3a8a3G1LJPHnG7nCUUsppmiAugWE9IziTX8hnq1PsDkUppZymCeISaNe4DvGRDUlcsZfCQh2sVkpVDZogLpFhPcPZc+QUv+48YncoSinlFE0Ql8iAqCY0DKzFzOV77Q5FKaWcUq4E4SjP/byIvCQiLS6+hyri5+PNkLjmzN9yiENZuXaHo5RSF1XeFsR7WPNFHwQSXR9O9XZnfDgFhYaPV+2/+MZKKWWz8iaIcGPMX4wxbwDijoCqs4jgQHq3DWHWyn3kFxTaHY5SSl2QUwlCRLqJSDegtojEOp4Huje06umunhGkHs9l4bYMu0NRSqkL8nFyu1cd/00DXnM814kOKqB/h0Y0qetP4oq9XNOpsd3hKKXUeTmbIO40xqS6NZIawsfbi6HxLfjnTzvYn3mKFg0D7A5JKaXK5OwYxLdujaKGGdojHC8RPlqp9ZmUUp5L74OwQZN6/vTv0IhPVu3ndH6B3eEopVSZnE0QXUQkq8TjhIhkuTWyau6unhEcOXmGHzYdsjsUpZQqk7MJYoMxpm6JRx1jTF23RlbN9WoTQkRwAIl6Z7VSykNpF5NNvLyEO+PDWbE7kx2HTtgdjlJKncPZBHGrW6OooW7r3pxa3l4krtDBaqWU53E2QTwtIvWLXohIAxGZ5qaYaozgID8GRjfhs99SyDmjg9VKKc/i9CC1MeZY0QtjzFEg1j0h1Sx39YzgRG4+X687aHcoSil1FmcThJeINCh6ISINcf4mO3UBcRENaNc4iMQVOlitlPIsziaIV4FlIvJ/IvI88CvwkvvCqjlEhGEJEaxLOc6GFK1eopTyHE4lCGPMh8Bg4BCQCgw2xsxwZ2A1yaBuzajt662tCKWURynPZa6+WCW+xfFcuUhdf19uiWnKl2sPknnyjN3hKKUU4Hy574ewJggKARoBM0VkgjsDq2lGXhFJQaHh7v+s4KgmCaWUB3C2BTEaSDDGPG2M+SvQE7jXfWHVPB2a1GXq8O7sSM/mjneXcyT7tN0hKaVqOGcThAAlL9QvQGeUc7m+7RvxnxFx7D58kjveXU7GCU0SSin7OJsg3gdWiMgzIvIMsBz4j9uiqsF6tw3l/VE92J+Zw9Cpy0jPyrU7JKVUDeXsVUyvAaOATMdjlGNeauUGl7cOYfqoHqQez2Xo1OWkHdckoZS69JwdpA4HDgNzHY8jjmXKTRJaBTNjdDzpJ05z+9RlHDyWY3dISqkapjwzyn0LbAG+cTz/xl1BKUv3iIbMGB1P5skz3D51GfszT9kdklKqBnG2iynaGBMNbDfGdHG87uLm2BQQG96AxDEJZOXkM3TqcvYd0SShlLo0yjsfhCnPxiIyQES2iUiyiPy5jPURIvKTiKwXkUUi0rzEunAR+a+IbBGRzSISWc5Yq40uzeuTOCaBU2fyuX3qMnYfPml3SEqpGsDZMYjBIjIYqF/03PH6Qvt4A1OAgUAn4A4R6VRqs1eADx2tkeeAF0qs+xB42RjTEYgH0p16R9VUVLN6fHRvT07nF3L7v5exMyPb7pCUUtWcsy2ImxyPxSWe33iRfeKBZGPMLmPMGWA2cEupbToBCxzPFxatdyQSH2PMfABjTLYxpsb3rXQMq8vssT0pNHD7v5frTHRKKbdydgxiVBmPey6yWzNgf4nXKY5lJa3DKgIIMAioIyLBQDvgmIh8LiJrRORlR4vkLCIyVkSSRCQpIyPDmbdS5bVrXIfZY3viJTB06nK2pmXZHZJSqpoq1xiEiMSLyC8islJErnbB+R8D+ojIGqAPcADrLm0foLdjfQ+gFTCy9M7GmKnGmDhjTFxoaKgLwqka2jQK4uNxl+Hr7cUdU5ez6aCWCVdKuV55B6lfBf4KjANevMi2B4AWJV43dywrZow5aIwZbIyJBZ5yLDuG1dpY6+ieyse696JbOWOt1lqGBPLxuJ4E1PLhzndX6FwSSimXK2+CCDTG/GSMWQNcbExgFdBWRFqKSC1gKPBVyQ1EJEREimJ4EphWYt/6IlLULOgHbC5nrNVeRHAgs8f2pI6/D3e+t5y1+49dfCellHKSs1cxTRKRSUAjx/NHOXc84SyOX/7jgR+wbrD7xBizSUSeE5GbHZv1BbaJyHagMfA3x74FWN1LP4nIBqzCgO+W+93VAC0aBvDxuMtoEFCLu99bweq9R+0OSSlVTYgxF7+1QUSeLmu5MeZZl0dUQXFxcSYpKcnuMGyTejyHO99dQXpWLu+Piie+ZUO7Q1JKVQEistoYE1fmOmcSRFVQ0xMEwKGsXO58dzkHj+UybWQPLmsdbHdISikPd6EE4WwX0/qyHq4NU1VW47r+zB57Gc0b1GbU9JX8vOOw3SEppaowZwepvfn9BrmSD+VhQuv4MXtsTyKDAxn9wSoWb68Z94copVzP2QSRDxwDDhlj9hY93BiXqoTgID8+urcnrUODuPeDJBZsPWR3SEqpKsjZBFEPWA/sF5HDIvK1iLR2Y1yqkhoG1uKjexNo36QO42as5r+b0uwOSSlVxThbaiPSGBNhjAnFurz1Y2C6OwNTlVc/oBYzxyTQuWk9Hkj8je83ptodklKqCinvjXIYY04bY2Zi3VWtPFy92r7MGB1P1xb1efCjNXyz/qDdISmlqggfZza6WGlv5dnq+PvywT3x3PP+KibOWkNBoeGWmAve56iUUs4lCKwupc3Aaqy7msGaPOhzdwSlXC/Iz4fp9/Rg9PQkHvl4LfkFhlu7N7/4jkqpGsvZLqYoYBsQBLzoZLlv5WECavkwbWQPrmgTwmNz1vHJqv0X30kpVWM5O0i9zRjzR+AfwGsi8q6IaB9FFVS7ljfvDo/jyrah/Omz9SSu0KuVlVJlc3YMYjK/z0e9C2vuhh1AgJviUm7k7+vN1OHdeWDmbzz1xUYKCg3DL4u0OyyllIdxdgyidJGjml30qBrw8/Hmnbu6M/6j3/jrl5vIKzCM7tXS7rCUUh7EqQRhjPmg6Lljbgc/Y4xOiFzF1fLxYsqwbjw0ew3/981m8gsKGddH739USlmcLdb3sGPu5+HAdmCHiDzu3tDUpeDr7cWbQ2O5qWtTXpi3lSkLk+0OybPkHIPT+ltI1UzOdjGNx5oRbgEQCeRidTO97J6w1KXk4+3F63/sio+X8PIP2zh5Op9xfVpTr7av3aHZqyAP3usPJw5B/L1w2YMQGGJ3VEpdMs4miCxjTJKI7DTGZAKISK4b41KXmI+3F68MsZLE24t28u7SXfRqE8LA6DCu6diYBoG17A7x0lv7ERxJhsje8PPrsOJfEHcPXD4B6jSxOzql3M7ZGeWOAUuAKx3/FaCXMaaBe8Nznk4Y5BrGGNbsP8b3G9P4bkMqKUdz8PYSLm8dzMCoMK7t3JiQID+7w3S//NPwZjcrEYz5EQ7vgKWvwoZPwcsHuo+AKx6CenqzoaraKj2jnIj0KWu5MWZxJWNzGU0QrmeMYeOBLL7bmMq8DansOXIKL4H4lg25PjqM6zo3oXFdf7vDdI+V78J3j8HdX0Drfr8vz9xltSbWzrJex9wJvR6BhnoFmKqadMpRVWnGGLamnWDehlTmbUxjR3o2ItA9vAEDo8MYGNWEpvVr2x2ma+TlwD9joGErGPUdiJy7zbH98Msb8NsMKMyHLrdD70kQ0vbSx6tUJbiiBdETmAx0BGphzTB30hhT15WBVoYmiEtrx6ETzHN0Q21Ns67yiWlRn4FRTV6k3h8AABpDSURBVBgYFUZ4cBW+h/LXt+C/T8HI7yDyigtvm5UKv06GpGmQnwtRg6H3Y9C406WJValKckWCSMK6iulTIA4YDrQzxjzpykArQxOEfXYfPsm8janM25DGhgPHAYhqVpeBUVbLolVokM0RlsPpbPhnV2gSBcO/dH6/7AxYPsXqmjqTDR1uhCsfh6Yx7otVKRdwSYIwxsSJyHpjTBfHsjXGmFgXx1phmiA8w/7MU9YA98ZU1uw7BkCHJnUYENWE66PDaNsoCCmry8ZTLH0VfnoORv8ILXqUf/9TmdbVTsv/BaePQ9vrrERRkWMpdQm4IkEsAa4G3gPSgFRgpDGmqysDrQxNEJ7n4LEcftiUxrwNaazam4kx0Do0kOujwxgQ1YROYXU9K1nkHoc3ukCLBBj2SeWPtfJdWDYFcjKhVV+48k8X77JS6hJzRYKIAA5hjT88gjVH9dvGGI+57VYThGdLz8q1ksXGNJbvOkKhgYjggOJuqC7N69mfLBa9CItegLGLXdc1dDrbGp/4dTKcTIfwy6HP49DqqrIHv5W6xFxyFZOjBlMHrKqu24wxZ1wXYuVpgqg6jmSf5r+bDzFvYxq/Jh8mv9DQrH5ta4A7ugmxLRrg5XWJvzxPZVpjD636wO0zXX/8vBz47UP4+Q04cRCaxVldT+2u00ShbOWKFsQNwL+AnVg3ybUExhlj5rky0MrQBFE1HTt1hvmbD/H9xjSW7jjMmYJCGtf1Y2BUGLfENCU2/BLdi/njs9b9Dff/6t4rkPJPW3do//waHNsHTbpYiaLDjeBV7inilao0VySIrcCNRV1KItIa+NYY08GlkVaCJoiqLys3jwVb0vluQyqLt2dwOr+Qy1sHM7F/W3q2CnbfibMzrNZD+wFw2zT3naekgjxY/4k1KJ65E0I7wpWPQedB4OV9aWJQCtckiFXGmB4lXguwsuQyu2mCqF6yT+cze+U+/r1kFxknThMf2ZCJ/dtyRZtg149V/PAULH8bHlx56W90KyyATV/AkpchYysEt4Hej0L0EPCu4cUS1SXhigTxDhABfII1BjEE2Af8CGCM+dxl0VaQJojqKTevgNkr9/GvxbtIy8olNrw+E/u3pW+7UNckiqxUeDMGOg+GQe9U/ngVVVgIW7+2EkXaBqgfYZXwiLkTfGpA7StlG1ckiPcvsNoYY+6paHCuogmiejudX8CnSSm8s2gnB47l0KV5PSb0a8vVHRtVLlF8+xisfh/GJ3lGPSVjYPsPsOQlOLAa6jaDKx6GbneDbzUpZaI8itZiUtXGmfxCvliTwpSFO9mXeYpOYXWZ0K8N13VuUv4rn47th8ndrF/pN/3TPQFXlDGwc4HVoti3DAIbWXd3K0utIOg+0iqkqFeBVUqFE4SIvHmhAxtjJlYyNpfRBFGz5BcU8uXag7y1MJndh0/SrnEQ4/u15YboMLydTRRfTYR1s2DiGs8u273nZ1j2tnUfhbIc2wfZh/RyYReoTILYC/z1fOtLzlVtN00QNVNBoeGb9QeZvCCZ5PRsWoUGMqFfG27q0hQf7wtcNpq5CybHQY8xcP1Lly5g5RrnXC4c7bhc+Ca9XLicKpMgPKre0oVogqjZCgsN8zamMXnBDramnSAiOIAHr2rDoNhm+JaVKL64z7p66KF1OjtcVaaXC1fahRLExVJt9RigUNWel5dwQ5cwvpvYm3/f3Z0gPx/+NGc9V72yiI9W7ON0fsHvG2dsh/UfW/NMa3Ko2rx9IXYYjF8Ft/7HWvbZaHirB6xJtBKIqrCLtSB+M8Z0u4TxVJi2IFRJxhgWbkvnnz8ls27/McLq+XN/39b8Ma4F/nPHWFcKPbweAkPsDlW5UmEhbP3GugosbQPUD3dcLjxMLxc+j8p0MRUAJ8tahXV5q04YpDyaMYalOw7z5k87SNp7lMuDUvko/1HyLp+E77VP2x2ecpcyLxd+CLoN18uFS6lwF5MxxtsYU7eMRx1nkoOIDBCRbSKSLCJ/LmN9hIj8JCLrRWSRiDQvtb6uiKSIyFsXO5dSZRERrmwXyqf3XcZH9ybwsM9nZJkArl3RhX8v3snJ0/l2h6jcQcQqnTLmJ2te8foRMO9PVjn3X960quyqi3LbcL+IeANTgIFAJ+AOESldBe0V4EPHJETPAS+UWv9/wBJ3xahqDhHh8tr7ic/9lazYcTRv2pQX5m2l1z8WMGVhMidyta+6WhKx7pW4Z541hWzjTjD/L/BGNCx5xZq3Q52XO68HiweSjTG7HKXBZwO3lNqmE7DA8XxhyfUi0h1oDPzXjTGqmmTB36B2A5oPmMSM0Ql8dv/ldG1Rn5d/2Eavfyzknz/u4HiOJopqK/IKaxrZ0fOheQ9Y8H9Wolj4d6vcuzqHOxNEM2B/idcpjmUlrQMGO54PAuqISLCIeAGvAo+5MT5Vk+xfCcnzrX5of6t3tHtEA6aPiuer8VfQI7Ihr/+4nV4vLuDV/27j6EmPmu5EuVKLeGvGwLGLIbI3LP6HlSjmP21V9lXF7L6j5DGgj4isAfoAB4AC4AHgO2NMyoV2FpGxIpIkIkkZGfoPqy5gwfMQGArxY89Z1aV5fd4bEce3E3vRq20Ikxck0+sfC3hx3lYOZ5+2IVh1STSNgaGJcP8y607sX/5pJYrv/wdOpNkdnUdwWy0mEbkMeMYYc53j9ZMAxpjS4wxF2wcBW40xzUUkEegNFAJBWFOdvm2MOWegu4hexaTOa/dS+OBGuO4FuOyBi26+Le0Eby1M5pv1B/Hz8SK2RQOPqOLQpJ4/bRvVoW2jINo2DqJ5gwDny4pUYcYYUo/nsiM9m+T0bJLTT3Ak+wy92oYwoHMTGtX1d82JDu+Apa9Z98h4+VgFEq94GOq3cM3xPZQtxfpExAfYDvTHahmsAu40xmwqsU0IkGmMKRSRvwEFxpi/ljrOSCDOGDP+QufTBKHKZAy8PxCO7oGJa8HX+S+T5PRspi7Zye7DZV3pfWkVGjhwNIe0rNziZX4+XrQODaJNo6DipNGmUR0iggPKvnvcwxUWGlKO5rAj/QTJ6dnscDx2pmeTXeJqs4aBtQjy82Ff5ilEIC6iAQOjwhgQ1YSm9V1wCWvmbmt2wbUfWa9j7rDupWjYqvLH9kC2VXMVkeuBNwBvYJox5m8i8hyQZIz5SkRuw7pyyWBdrfSgMeZ0qWOMRBOEqqjkn2DmYLjhVavuUhWXlZtn/Yo+lH3WF2nK0ZzibXy9hZYhgbRtVIfWJZJHy5BA/HzsLz+RX1DI3sxT7DiUzc6MbHYcOmElgoxscvMKi7drVMePto2DaNuoTnESbNMoiOAg64a3HYdOMG9jGt9tSGVr2gkAYlrU5/roJgyMCqNFw4DKBXpsP/z6Jqz+AArzrUmcej8Koe0qd1wPo+W+Vc1kDLzbD04ehgmrwaeW3RG5zakz+exMP8mOdOvLtujLd++RkxQ6/sS9BCKDA60v28ZFLY86tA4NonYt1yeO0/kF7Dl8ypHErLiSD2Wz+/BJzhT8ngia1a99TiuoTaMg6tV2fka93YdPMm9jKvM2pLHhgHXpalSzugyMCmNgVBNahQZV/I2cSINfJ0PSNMjLgc5/sAoDNu5c8WN6EE0QqmbaNg9mDYWbJ1t30NZAuXkF7D580vHlfKK422bP4ZPkOzKHCDRvULt4fKN1iV/rdfwv/iWdm1fAzgxrfGDHod+Twd4jpygocY7whgGO49YpPn7rRkEE+fm49D3vzzzF9xvT+G5jKmv2HQOgQ5M6DIwK4/roJrRtXKdiBz55GJZNgZXvwpkT0OFG6DbCM+7M9qtjDbpXgCYIVfMUFsK/r4S8k9Zc0zq/81nyCgrZe+Sk4wvd8Th0gl2HT3Im//df92H1/GnT6PfWRnjDANKycq3uLce++4+eouhrxNtLiAwOsJKNo5XSplEQrUOD8Pe99N1bB4/l8MOmNOZtSGPV3kyMgdahgVwfHcbAqDA6htUp/4yEpzJh5VRrHnNPudGuWRzc+1OFdtUEoWqeTXPh0xEwaCp0vd3uaKqM/IJC9h/NKe4WKkoCyenZ5OT9XhG3lrcXrUIDixNHUbdVZHAgtXw8c4A8PSvXShYb01i+6wiFBiKCA4pbFtHN6pUvWeRmQdp68ITvUG1BXJgmCFWssADeudz6w31gmc4L4AKFhYYDx3LYn3mKJvX8CW8YcOEJmTzckezT/HfzIb7bkMqynUfILzQ0q1+bgVFNGBgdRmyL+uWfwraK0gShapb1n8Dn98KQ6dbEMUpdwLFTZ5i/+RDzNqbx847DnCkopEldfwZENWFgVBPiIhtW6/tNNEGomqMgH6b0AN8AGLdUp59U5ZKVm8eCLel8tyGVRdszOJNfSEiQH9d1bsz10WEktGxYpVtOZblQgnDt5QNK2W3dLGu+6aGzNDmocqvr78sfYpvxh9hmZJ/OZ+HWdL7fmMbnvx0gccU+GgT4cm2nJgyMbsLlrUM8drzFVbQFoaqP/DMwubs1S9y9C/CI+hiqWsg5U8Di7enM25jGT1vSyT6dT11/H3pENsTH2/7/z1qGBPHngR0qtK+2IFTNsGYGHN8HN76uyUG5VO1a3gyICmNAVBi5eQX8vOMw321MZfPBLLtDA3DbJcSaIFT1kJdrTQDToie06W93NKoa8/f15upOjbm6U2O7Q3E7TRCqelj9Ppw4CIP/ra0HpVykeo+wqJrhzElY+iq0vNJ6KKVcQlsQqupb+S6czICrEu2ORKlqRVsQqmrLzbJmAmtzNYQn2B2NUtWKJghVta34F+RkwlVP2R2JUtWOJghVdeUchV/fgvY3QLNudkejVLWjCUJVXb++BaePw1X/Y3ckSlVLmiBU1XTyiNW91HkQNImyOxqlqiVNEKpq+uUNyDsFfZ+0OxKlqi1NEKrqOXHIurQ1+o8Q2t7uaJSqtjRBqKrn59eg4Az0+ZPdkShVrWmCUFXL8RRImgaxwyC4td3RKFWtaYJQVcuSV6ypRK983O5IlKr2NEGoquPoHqukd/cRUD/c7miUqvY0QaiqY/FL4OUDvR+zOxKlagRNEKpqOJxsTScaNxrqhtkdjVI1giYIVTUsfhF8/KHXI3ZHolSNoQlCeb5Dm2HDHEgYB0GhdkejVI2hCUJ5vkUvQK0guHyi3ZEoVaNoglCeLXUdbPkKLnsQAhraHY1SNYomCOXZFv4d/OvDZQ/YHYlSNY4mCOW5UpJg+/dwxUTwr2d3NErVOJoglOda8DwEhED8OLsjUapG8rE7AOVhco7BvuVgCu2NI+sA7FoI1z4PfkH2xqJUDaUJQllOHoHlb8PKqXA6y+5oLHWaWjfGKaVsoQmipjtxCJZNhlXTIO8kdLwZ4u8Fv7p2Rwb1mkOtALujUKrG0gRRU2UdhF/+CaunW3MrRN0KvR+FRh3tjkwp5SE0QdQ0R/fCz6/D2kRrnKHLUOg9SedWUEqdQxNETXFkJyx9DdbPBvGCmGFWXaMGEXZHppTyUG5NECIyAPgn4A28Z4x5sdT6CGAaEApkAncZY1JEJAZ4B6gLFAB/M8Z87M5Yq630rbD0Fdj4GXjXgh5jrJIV9ZrZHZlSysO5LUGIiDcwBbgGSAFWichXxpjNJTZ7BfjQGPOBiPQDXgDuBk4Bw40xO0SkKbBaRH4wxhxzV7zVTup6WPIybPkafAPgsvHWo05juyNTSlUR7mxBxAPJxphdACIyG7gFKJkgOgGTHM8XAnMBjDHbizYwxhwUkXSsVoYmiItJWW0lhu3zrCuRej8KPR+AwGC7I1NKVTHuTBDNgP0lXqcACaW2WQcMxuqGGgTUEZFgY8yRog1EJB6oBewsfQIRGQuMBQgPr+FTUO5dBktegp0LoHYDuOopiB8LtevbHZlSqoqye5D6MeAtERkJLAEOYI05ACAiYcAMYIQx597aa4yZCkwFiIuLM5ciYI9iDOxeYrUY9iy1ylJc/Sz0GA1+deyOTilVxbkzQRwAWpR43dyxrJgx5iBWCwIRCQJuLRpnEJG6wLfAU8aY5W6Ms+oxBpJ/tOZoTlkJQU3guheg+0i9sUwp5TLuTBCrgLYi0hIrMQwF7iy5gYiEAJmO1sGTWFc0ISK1gC+wBrDnuDHGqqWwELZ9Z7UYUtdCvRZww6sQcxf4+tsdnVKqmnFbgjDG5IvIeOAHrMtcpxljNonIc0CSMeYroC/wgogYrC6mBx27/xG4Egh2dD8BjDTGrHVXvB6tsAA2fwlLXoH0TdCgJdw82brJzaeW3dEppaopMaZ6dN3HxcWZpKQku8NwrYJ82DgHlr4Kh7dDSDvo/ZhVFsPb7uEjpVR1ICKrjTFxZa3TbxlPlH8G1s2Cn1+Do3ugcRQMmW4V0vPytjs6pVQNoQnCk+TlwpoZVhG94/uhaSxc93doNxC8dG4npdSlpQniVCa8P9DuKCzZ6ZCTCS0S4MY3oE1/ELE7KqVUDaUJwssbQtvbHYUlLAZi7oSWV2piUErZThOEfz3444d2R6GUUh5HO7aVUkqVSROEUkqpMmmCUEopVSZNEEoppcqkCUIppVSZNEEopZQqkyYIpZRSZdIEoZRSqkzVppqriGQAe+2Oo5JCgMN2B+FB9PM4m34ev9PP4myV+TwijDGhZa2oNgmiOhCRpPOV3a2J9PM4m34ev9PP4mzu+jy0i0kppVSZNEEopZQqkyYIzzLV7gA8jH4eZ9PP43f6WZzNLZ+HjkEopZQqk7YglFJKlUkThFJKqTJpgvAAItJCRBaKyGYR2SQiD9kdk91ExFtE1ojIN3bHYjcRqS8ic0Rkq4hsEZHL7I7JTiLyiOPvZKOIzBIRf7tjupREZJqIpIvIxhLLGorIfBHZ4fhvA1ecSxOEZ8gHHjXGdAJ6Ag+KSCebY7LbQ8AWu4PwEP8EvjfGdAC6UoM/FxFpBkwE4owxUYA3MNTeqC656cCAUsv+DPxkjGkL/OR4XWmaIDyAMSbVGPOb4/kJrC+AZvZGZR8RaQ7cALxndyx2E5F6wJXAfwCMMWeMMcfsjcp2PkBtEfEBAoCDNsdzSRljlgCZpRbfAnzgeP4B8AdXnEsThIcRkUggFlhhbyS2egP4E1BodyAeoCWQAbzv6HJ7T0QC7Q7KLsaYA8ArwD4gFThujPmvvVF5hMbGmFTH8zSgsSsOqgnCg4hIEPAZ8LAxJsvueOwgIjcC6caY1XbH4iF8gG7AO8aYWOAkLuo+qIocfeu3YCXOpkCgiNxlb1SexVj3Lrjk/gVNEB5CRHyxkkOiMeZzu+Ox0RXAzSKyB5gN9BORmfaGZKsUIMUYU9SinIOVMGqqq4HdxpgMY0we8Dlwuc0xeYJDIhIG4PhvuisOqgnCA4iIYPUxbzHGvGZ3PHYyxjxpjGlujInEGnxcYIypsb8QjTFpwH4Rae9Y1B/YbGNIdtsH9BSRAMffTX9q8KB9CV8BIxzPRwBfuuKgmiA8wxXA3Vi/ltc6HtfbHZTyGBOARBFZD8QAf7c5Hts4WlJzgN+ADVjfYTWq7IaIzAKWAe1FJEVERgMvAteIyA6sVtaLLjmXltpQSilVFm1BKKWUKpMmCKWUUmXSBKGUUqpMmiCUUkqVSROEUkqpMmmCUEpdlKOC6koRWSAiLinjoDyfXuaqlFKqTNqCUB5HRCKLat2LSEcRWSciLRyvJznmAdgoIg+X2ifHcZPhPhF5y7F8kYjEOZ4/LyLZjucji7ZxvM4u8fxxEVklIutF5NkSy4c7lq0TkRki0rrEjY0FJZ43dZx3m2OOj+Ui0tRxjP6OonsbHHX9/cp4/21E5EfHeX5znEdE5GXH+94gIreX2P4Jx7J1IvKiY1lrEfleRFaLyFIR6VBi+7ccn9FaEcku8fmUuY+ITBeR2xzPx4iIEZGQyvwbqyrCGKMPfXjUA4gENmKVPF8LdHYs745192wgEARsAmId61oD6x3PRwJvOZ4vAuKARlh3n2Y7lg8HppQ4Z9Hya7HuzBWsH1DfYJXb7gxsB0Ic2zUsFXN2qddF5xXga+BmwB/YD7RzbPMhVmHG0u9/BTDI8dwfq6T1rcB8rPkPGmOVnAgDBgK/AgEl48KaE6Ct43kCVsmSouO/AwwvGeeF9sGaf+A2RyxJwKGiz0Ef1fvhc/7UoZStgoDvsb6kNjmW9QK+MMacBBCRz4HewBqgNpB7geP9BatExSzH6xRgrIh4GWNKlhW/1vFYUyKOtlgT9XxqjDkMYIwpXY+/LImAH5AF/Ai0xyo0t92x/gPgQazy5jjeUx2gmTHmC8d5ch3LewGzjDEFWIXZFgM9gD7A+8aYU0VxOaoCXw58apUrAkccRYIoNZ+AE/vgiPUD4FEn3ruqBjRBKE/VArgLeFJEOhpjLlaQrSnnnzgmEogyxkwo8eW3CNgGbBCRvBLbCvCCMebfJQ8gIhPKFz4Aw4wxSSLyPPAw8G0FjlERXsAxY0zMeda3xEqQ5dmnLlbxxCvQBFFj6BiE8lRbjDGzsArV/dtRuXMp8AdHJc9AYJBjGcAQ4JfzHOtpx6OYMabQGDPaGNO51JfiD8A9jl/UiEgzEWkELACGiEiwY3nDcryXLCAEKyFFikgbx/K7gcWl4joBpIjIHxzn8RORAMf7vF2subpDsbq9VmJ1O41ybIOINDTWXCK7RWSIY5mISFfH8wisrql1pc573n0cHgEmG2POlON9qypOE4TyaMaYxcBW4H5jTcs6HeuLcQXwnjFmjYi8hDUuMeU8h0kx1jSNzpzvv8BHwDIR2YBVObSOo5vrb8BiEVkHOFOWPVFE1mIlssmO7qJRWN04G7BmzPtXGfvdDUwUq3rrr0AT4AtgPdYX+wLgT8aYNGPM91ilnpMc53rMcYxhwGhHrJuwJtkBWAUEA2sc28cBL19kH7BaVjV5Xo4aSS9zVaoGEZFFxpi+pZbNMcbcZlNIyoNpC0KpmuW5Mpa9fsmjUFWCtiCUUkqVSVsQSimlyqQJQimlVJk0QSillCqTJgillFJl0gShlFKqTP8PIYtMGQOWOT4AAAAASUVORK5CYII=\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 23,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "scrolled": true,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:29:02.887Z",
          "iopub.execute_input": "2020-02-28T13:29:02.897Z",
          "iopub.status.idle": "2020-02-28T13:29:03.225Z",
          "shell.execute_reply": "2020-02-28T13:29:03.253Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Кросс-валидация (перекрёстная проверка)\n",
        "\n",
        "*Перекрестная проверка* (*кросс-валидация*) представляет собой статистический метод оценки обобщающей способности, который является более устойчивым и точным, чем разбиение данных на обучающий и тестовый наборы. В перекрестной проверке данные разбиваются несколько раз и строится несколько моделей. Наиболее часто используемый вариант перекрестной проверки – *k-блочная кросс-валидация* (*k-fold cross-validation*), в которой k – это задаваемое пользователем число, как правило, 5 или 10.\n",
        "\n",
        "![crossval-1](https://bitbucket.org/despairr/ds-course-2018/raw/443959e3b5e41168ceba4adcffbe1e9e9084f246/intro-to-ml-images/927.png)\n",
        "\n",
        "В scikit-learn перекрестная проверка реализована с помощью функции `sklern.model_selection.cross_val_score`."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# создадим классификатор kNN (n=1)\n",
        "clf = sklearn.neighbors.KNeighborsClassifier( n_neighbors = 1 )\n",
        "\n",
        "# получим метрики на кросс-валидации на датасете с раком груди\n",
        "scores = sklearn.model_selection.cross_val_score( clf, cancer.data, cancer.target, cv = 3 )\n",
        "\n",
        "scores"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": [
              "array([0.88421053, 0.92631579, 0.9047619 ])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 25,
      "metadata": {
        "execution": {
          "iopub.status.busy": "2020-02-28T13:29:57.992Z",
          "iopub.execute_input": "2020-02-28T13:29:58.002Z",
          "iopub.status.idle": "2020-02-28T13:29:58.023Z",
          "shell.execute_reply": "2020-02-28T13:29:58.034Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"Средняя правильность перекрестной проверки: {:.2f}\".format(scores.mean())"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": [
              "'Средняя правильность перекрестной проверки: 0.91'"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 27,
      "metadata": {
        "execution": {
          "iopub.status.busy": "2020-02-28T13:31:47.478Z",
          "iopub.execute_input": "2020-02-28T13:31:47.488Z",
          "iopub.status.idle": "2020-02-28T13:31:47.509Z",
          "shell.execute_reply": "2020-02-28T13:31:47.520Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# создадим классификатор kNN (n=1)\n",
        "clf = sklearn.neighbors.KNeighborsClassifier( n_neighbors = 1 )\n",
        "\n",
        "# получим метрики на кросс-валидации на датасете с ирисами\n",
        "scores = sklearn.model_selection.cross_val_score( clf, iris_dataset['data'], iris_dataset['target'], cv = 5 )\n",
        "\n",
        "scores"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/plain": [
              "array([0.96666667, 0.96666667, 0.93333333, 0.93333333, 1.        ])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 29,
      "metadata": {
        "execution": {
          "iopub.status.busy": "2020-02-28T13:32:48.970Z",
          "iopub.execute_input": "2020-02-28T13:32:48.980Z",
          "iopub.status.idle": "2020-02-28T13:32:49.000Z",
          "shell.execute_reply": "2020-02-28T13:32:49.010Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Средняя правильность перекрестной проверки: {:.2f}\".format(scores.mean()))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Средняя правильность перекрестной проверки: 0.96\n"
          ]
        }
      ],
      "execution_count": 31,
      "metadata": {
        "execution": {
          "iopub.status.busy": "2020-02-28T13:33:08.578Z",
          "iopub.execute_input": "2020-02-28T13:33:08.589Z",
          "iopub.status.idle": "2020-02-28T13:33:08.612Z",
          "shell.execute_reply": "2020-02-28T13:33:08.623Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Преимущества кросс-валидации\n",
        "\n",
        "**Равномерное распределение примеров** (тяжёлые / лёгкие, сильно похожие / максимально не похожие):<br>\n",
        "Обычный train_test_split выполняет случайное разбиение данных. Представьте, нам «повезло», и все трудно классифицируемые примеры в конечном итоге попали в обучающий набор. В этом случае в тестовый набор попадут только «легкие» примеры, и правильность на тестовом наборе будет неправдоподобно высокой. И, наоборот, если нам «не повезло», все трудно классифицируемые примеры попадают в тестовый набор и поэтому мы получаем неправдоподобно низкую правильность. Однако при использовании перекрестной проверки на каждой итерации в тестовый набор, использующийся для проверки модели, попадают разные примеры. Таким образом, модель должна хорошо обобщать все примеры в наборе данных, чтобы все значения правильности (или их среднее) были высокими.\n",
        "\n",
        "**Чувствительность модели к разным данным**:<br>\n",
        "Кроме того, наличие нескольких разбиений дает определенную информацию о том, насколько наша модель чувствительна к выбору обучающего набора данных.\n",
        "\n",
        "**Используем данные более эффективно**:<br>\n",
        "Используем все 100% и для обучения и для теста."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Стратифицированная k-блочная кросс-валидация\n",
        "\n",
        "![crossval-2](https://bitbucket.org/despairr/ds-course-2018/raw/443959e3b5e41168ceba4adcffbe1e9e9084f246/intro-to-ml-images/957.png)\n",
        "\n",
        "В стратифицированной перекрестной проверке мы разбиваем данные таким образом, чтобы пропорции классов в каждом блоке в точности соответствовали пропорциям классов в наборе данных.\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# создадим классификатор kNN (n=1)\n",
        "clf = sklearn.neighbors.KNeighborsClassifier( n_neighbors = 1 )\n",
        "\n",
        "# создадим объект для стратифицированной КВ с 5 фолдами\n",
        "cvgen = sklearn.model_selection.StratifiedKFold(5)\n",
        "\n",
        "# натравим кросс-валидацию на ирисы\n",
        "scores = sklearn.model_selection.cross_val_score(\n",
        "    clf, iris_dataset['data'], iris_dataset['target'], cv = cvgen\n",
        ")\n",
        "\n",
        "list(scores), scores.mean()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "text/plain": [
              "([0.9666666666666667,\n",
              "  0.9666666666666667,\n",
              "  0.9333333333333333,\n",
              "  0.9333333333333333,\n",
              "  1.0],\n",
              " 0.96)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 35,
      "metadata": {
        "execution": {
          "iopub.status.busy": "2020-02-28T13:35:20.433Z",
          "iopub.execute_input": "2020-02-28T13:35:20.443Z",
          "iopub.status.idle": "2020-02-28T13:35:20.464Z",
          "shell.execute_reply": "2020-02-28T13:35:20.474Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Кросс-валидация и кастомные метрики"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate a random n-class classification problem.\n",
        "X, y = sklearn.datasets.make_classification( n_classes = 2, random_state = 0 )\n",
        "X.shape, y.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 36,
          "data": {
            "text/plain": [
              "((100, 20), (100,))"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 36,
      "metadata": {
        "execution": {
          "iopub.status.busy": "2020-02-28T13:36:59.654Z",
          "iopub.execute_input": "2020-02-28T13:36:59.664Z",
          "iopub.status.idle": "2020-02-28T13:36:59.687Z",
          "shell.execute_reply": "2020-02-28T13:36:59.697Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g = matplotlib.pyplot.scatter( X[:,0], X[:,1], c = y )"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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Jy6t9jQ6vQxePgsCcyM/2Pqis+1GWlgmKOnUkLJFrrccB4yBSxjZR121M/v71H6x2CwFf5eOhQIif/hu/4YlJz0zj03GRDYCDFV0l3348l8y8DK556opqX3fMeUexd8e9+OSZaWz6Zws9TjyEU4YcT1qmmwGD+zFgcL/dXnuf/ffi2R8e4tXb32PJt8vJbp7F+SMHcsJlfdE6gC66GnSVLc88b6EdfVCO2F8OVemyZ9mexLfzgudtdMZ/wGgGyhbpRqnEAZYddxE//XcxoRi1VgyLwepla+h4WM1anI1dwZotvHrru5UG0f0eP5+Nm8mxg3rTqVd0YTGt/egt54K5he31YgLforecD/kzpKZ6FXKvkkJy98ohtNN8322UghZt47fycuLjU6Pmk/u9Aaa9MpPzbz2Dj56YytzPFpLZLINzbjyN3mfsaKXud0h7Rrx2bZ2u37Zza+775Jao49r/QzWv8KG9E2ucyAmtjH1cKQgXgPM4KLk7xuMGyjVw+4/5rfP4e+ma6NMHQuS2zK5ZLE3A3M8WoozoNRoBX4BvPpoTM5Hjm14xa2jnLrZwZDqofxY4B9RbvKlIEnkKadmuOQce0ZFfv19RKaHbXQ7OHTFwF6/cM6VbY88Z93sDXHPYSEq2lG2//h8LVzHoljO46I5z4nb9aunogdSKB2K0nnfB2hECm2I/ZmmOUg7IfQdddA2EN0cSvHKjsp+qNO3w3BEDWfzN8qgvPavdyj8r1u1yAHrL+q189cH3eEq99Dype437i1ORxWrEXGxnGAqrPXYK0qG/ou+8ALQPQn/FN8BGIF7TD8cDPwAHKKXWKKX+HY/zimj3fHwzhx5/MDaHDWe6k/ScNG56eSgHHXVA3K7R+chqzqWhcENRpS8RX7mf9x76mLKiBJT/tR8RqdwXxY1ynVrj06j064Gq0yldkHZlJIkDynYAqtlMVLOPULnjUfnfonbqHwc49LiDueapy7E7K48beEq83HnaI/w0a3HM638/eR6X7jeMV29/l7fvncDwvnfz2L/HNtoCZUcN7IlpRr83i83KsYP6xHyNsnUEFWM6pHKCNX6f9cZCtnpLUUUFxZQWltGqQ0ss1ujZInXx56K/uOFfd+L3+NExfgGrcme6ue+TkXTrGz2dMN5Mz2QouRMIVfznAse/UNnP7NHSeR34MTJrJfQ7GLmQNhTlvniPyjSYpolSivP2upKiTdFz3Dv22Jfnfxxd6Zi33Md5La+M2ozCmebgzgnD6XVS9xpff3fW/LaOKWOns37VRg497mBOvKIf7oza7SVaV/+b8B2PXjEWw1CRpK41l98/iHOHnx7z+VoH0ZtPhvA6YNudmA0sbVDNPo3UkGmCqtvqTRK5iGnNb+sYe8PrzP9y0W6TucNtZ+z8MbTplJhCSzq0Gu2dBLoU5TgO7EckrE5OOBzmnfsmMumZaZQXe2jTeR/WLF8bs8XpcDv4tOydSsd+mDqfRy55Bk+JN+r5fQf1ZtR7N8QlzvlfLuKes8YQCoQJh8I43Hay8jN5Yf4YMvMy4nKNPVVUUMz3k+cTCoQ44rTDdltRU5tb0aVjwPcFoMB5MirjZpSx66mwjVl1iVz6yEVM+3RsRd/ze7P42+X4yqrO8NjBYrPQ7qDWCUviAMraBpWRuOX6O3v+utf48s3/bV99ujrGYOc2zdtEJ6pdfeEYMQYEa8M0TcZc/lylFbJ+T4DC9UWMf2QSQx+NzzTVPZWdn8XJVx5X4+crIweV9TBkPVyPUTUOjaOMm6gX+x+2Lzoca6uwyAYKdqeNA4/oyP1Tb0twZMlRVlTO9NdnR+8upYgqy+tw27n8vvOjznFIvy4x73CcaQ76X9o3LnGuX7kxZos/FAjx7cdz43IN0bBIi7wRKSoo5t37P+K7yT/iznAxcNhJnDLk+FrX/m7fpQ2H9u/Kwhm/bK9PbrVZyGmZzR0TbiJ/7zzy96l7aYBUsWn15pgbMaAhLdeNGTLxlfvJyE3n3w9fyNHnHBl1Dqfbwaj3b+T+8yL13UPBMFarheMvOYbD+sennrkr3Rm1MnabtMwYW7+JlCeJvJEoL/Fw9WEjKdpYvH2Ryksj3mLFvD8Y8eo1tT7vXR8O58PHpvDZuJkEfAF6n9mLy+49f7dL9lOF1kF0+WvgGQ/aC85+qPSbYlY3bNEuP/Y8fkPR/dgujHr/Rnzlflzpzl12oRx+8qG8s2os30ycg6fUR88Bh7Bv17Zxe0+5LXPo2GNfls35vVJCd7gdDBwm868bIxnsbCQ+enIqr9/x/vaW8zY2p43Xlj5Fy3bNkxRZw2ZuHQb+r9mx0tMKRg6q2XSUkR71/BdveoNPx82sNHfc4XbwzPcPxjUZ19WW9VsZ2f8+ClZvRhmKUCBE/8uO4fqxQ5r8BiqpTAY7GwitTQj9CcoKlnZx+6X6+X+/RiVxAJvdyu8LVkoij0GH/gT/V8DOUwFDYJahvRPRrktZMe9PfOU+DjyiI063gyGPXUpOy2wmPjGV0sIy9uvenmueuqJBJXGAvL1yeGXxEyyb+ztb1hZyQM8ONG/TOGqoi2iSyBNIB+ahi26ILD3WJlhaQs5YlLXuGz7vvV9LLDYL4Sq1P0xTk9+66fRj75HgssgXqvZXecDLX4vmcsegOZQVl6OUQTgU5oYX/4/jLz6G80eewfkjz0hKyHtCKUXnIzomOwyRADJrJUF0eBN665WR8qjaA/gg/Dd6y8XoPVleXo3Trj4Rq63y97LFaqFlu3wO6Fn3L4pGybI3saochkN2bj2rmII1W/CW+vCUePB7/Dw1dByrlqxOfJxC7IYk8gTR3k8qyq5WOgr4wT+7zuffe7+9uH/KLeTvk4fDZcfmsHLwvw5kzIy7pE+0OrZDwNKaqjemP3+Xid8bvXIwGAgxbdzMBAVXe9oswix7GXPrdZhlY9HhLckOSdQz6VpJlPAGKvfFVtAhMDfH5RLd+x3Mu3+/wKbVm3GmOeK+m05jo5SC3DfRRbdC4LvIQUsbykKXoPk86vlm2GTrpuJqz6fDGyJ3W5Z2e1QuIJ50aDV6yzmR4lL4wD8bXf4q5H0Qly480TBJIk8Q5Tgc7fs4RkU3Bbb4bQ2mlKJF24Y5qDXn0/mMG/kOBWu20Lx1Hlc/cTk9TjwkqTEpIxeVOw5tlgMBlJHDwccWEgp8GvVcZ5qDo06PmjCADm9AF10X6XPHACMNsh5BOY6u/zdQNZbSByOlXreXf/WDDqCL70LlvZfweERiSNdKojiOA8u+VK665wLHsSjbgcmKKko4FGb+l4uY/f53bF5XGLfzTh77BXedMYZ/lq/FV+Zj9bK13HbSg3z01NS4XaMulJGGMiJb5TVrlct5I0/HmbZjhx+H20G7g1rzr3OOqPQ6rTW68FIILiZyx+UFczN6638ipVgTzf89lWt4A2gILkTr6E0wROMg88gTSGsfuvxt8E0B7Cj3+eA6u8FUcvt76T/cfNy9+D0BNJpQIMw5w09l8AMX1um8oWCIgdmXEYgxPdKwGEwufqtBbos2/8tFfPridMpLvPQ97yj6X9YXu6NyyVodWIje+u+KTRB2ZgX3xRiZtycuYMDceBjo0hiP2FAtlsh4SYqTeeQNgFJOVPr/Qfr/JTuUKFprbj/5IbZurNwHPOnpaRzc50B6Dqh9edUNfxUQCsTeFEKbmmVzfqN7v4Nrff760uOEbvQ4oduun2RuAmIlxxCE/6mPsHbNdRZ43qfyeIwdXKdIEm/EpGtFAPDb/D8pLYzeGchX7mfy2C/qdO6sZhlUe+OnwJlWdZOHFGLrWs3ORS6w9054OCpjONgPBZyRjRmUC2ydURl3JjwWkTiSyAUQSdix9lUEWDjjF76pQ9W8jJx0uvTuFPux7DQO6Fn/25xpHUaHt8Rlzv7OlKUVuM6MJMzt7GDJQ7nOjOu1ahSPcmLkvonK+wCVeR8q912MvAkxyw2IxkMSuQCg0+H7xdwcASDoDzH60meq3bqsJh76fBStO7WqdMyZ5uDRWffUujpjTZmeCehNR6IL+qI39sQsGRPXgT+VeS8q426wHgSWtpB2OSrvY5QRY6uyBFG2A1Gu01C2LkmLQSSODHaK7WaN/4bHr3yBgDd2f3aXPp148uv763SNVUtWM+/zn9jngFb0Oql71GrUeNO+L9FFI9hRFAvACe5LMDJvrtdrCxFvMtgpdqvfBf8iq1kmo055mHAousW69o8Ndb5G+y5taN+lTZ3PU1O67FkqJ3EiP3vfQWdcj1L2hMUidtDaRJe/DOWvgS4G6wGozDtQ9p7JDi0lSdeKqKTrMZ2xu2wxH+vQrV1ig4mH8PrYx3UYdPTgrkgMXToaysaC3gqYEFqGLvw3OvhrskNLSZLImxDTNPlqwvfcfvKD3H7yg3w14XtMs/LiEZvdxkWjzo6a1+1w2bn8/kGJDDc+bJ1jH1dpoBrH5hipRptlkY08qLodnb/iDkrsKelaaSK01jx04dPM/WwBvvLIHOPF3yzj20lzGTX+xkrPPe/mgWQ2y2D8Qx9TuKGYDoe0Y+ijl3BAj/qfXRJvKn04uvASovrIM0Y0mIVYTY65AZQlRuFJDaHfkhFRypNEngDaLIPQMjDyUNZ9kxLD8h//qJTEITLlcM6nC1j+4+906rX/9uNKKU4afBwnDa75jufJMuOtr3j7/g8pXL+V9ge3Zeijl9Clz46SB8reDXLfRpc9HqmFYmmFSh+GcvZPYtRNnNEyUiwuigLL/jGOi92RrpV6Zpa9gt50FHrrUPTmMzA3n40Ox6fa4Z5YOPOX6E2DgYAvyIIZvyQ8nnj4+JnPePqal1n/50b8ngDL5/7OrQMeYOkPKyo9T9m7YeS+hdFiHkazyZLEk0wZ6eAeBLiqPOJAZfwnGSGlPEnk9Uj7/wfbZk3ossj/Q8vQRdcmPJaMnHRsjugbMJvDSmZu7MUi09+czaX7XcvJrgu5usdIfp69pL7DrLFwKMxbd0+otHcmgN8T4PU73k9SVKKmVMZtkD60YpxCRWat5Lws895rSRJ5PdLlrxM9oBOC4FJ0aE1CYznmvCNj1tpQSnHMeUdFHZ/07DSevfZV1q/cRNAf5I+Fq7jj1If55euliQh3t4oKSgjG2NEeYOUvf9foHCWFpbx17wT+c+Tt3Hfe41EteVF/lDIw0q/BaDEP1WI5RrOpKMfhyQ4rZUkir09mNWVglQ10UUJDyWqWyX2TbyE9Ow13pgt3pov0nDTum3wLmXkZlZ4bDod58+4Polu73gCvjWoYNa0zctMxqikp0KpDi92+vqigmCFdh/PBI5+wfO7vfPvRHEb2v4/pb9Z9t6ZE2fTPZt64630evOBJpr74Jd7yqvPlU4MU86o7GeysT46+EFoFVK3vocGa+EGd7v0O5sONr7D0h8jMgM5Hdoy5srK0sKza1Z1/L03snUR17A4bZ153Mh8/Pa3SF47DbefSe8/f7esnPDqFks2l21v1Wke6ZcZe/zrHDuoTVa62oVny3XJuG/AA4WCYYCDED1MX8MHoT3h+3iOyM1QTJC3yeqTSrgAjB9i2elABLsgYhVLJqb9ttVnpenRnuh7dudrl8enZaVhtsafm7dW+cmtX6xA6tApd3d1HPbr8/kGcd/PpuDNcGBaDZvvkMeK1a+hZg12Hfpy2MGbXjNawelnNv6x+W/AnL414kxeHv8myub/vUfy1pbVmzGXP4iv3b38Pfo+fLeu38s79ExMSg2hYpEVej5SRC82mRjaT8H8NlhaotCtQ9sOSHVq1tFmKJTSbc67fjwlP/obfs+NuwuGuvCjI9H4OJXcDAdAhtP1wVPbjKCM7IbEahsGld5/HxXeeg98bwOl2RN2ma60h8COEVoC1Ldj7oJSF7OZZMe8uwsFQpcFfbZaAsqFU1RkW8OY9E/jwscmR2UAaPn1pBqddfQJDH700/m92J1vWFbJl3dao46FAiG8nzeXapwfX6/VFwyOJvJ4pIzsypSoFplWZ3plQfBNgcNG1YDWz+PCFvfGUhMjbO5ehj11Gr5MiG0zowCIovoVKC20Cc9Bbr0bljU9o3IZh4IpR01yb5ZFt2MJ/RuYtKxsYzSB3PGffeCor5v1RaV69xWahY48ONG+Tjw4sQhffBuG/AF1tQGsAACAASURBVIV29EVlPbj9S2rNb+uYMGYyAd+OLzq/x8/UsdPpf8kx7Nu1bb29X7vTXm2lyoa405Kof3HpWlFKDVBKrVBK/aGUujUe5xSJpc2iiiTuAzwoPAz6z3om/rqYKcUP8u5fL3DMuUfueH75a0QXowpCcAk6VLNZI/VNlz0RaYlrD5G7hnIIr0WXjOLI03pw4aizsTvtpGW5cbjt7N+9PXdNHIEOr0dvvQzCfwAhIAj+/6ELr2BbtdA5ny6IKm8AEAyE+H7KvHp9X5l5GXQ+siOGpfKvr8Nt59SrTqjXa4uGqc4tchVZ5/w80B9YA8xTSk3RWjeMeWpiO61NwESpGP/svpmgVNSyaaXC2MzPUeq6yg+E1lZzlQA6vA5lrb8WaY15pxA90BwC/zdoHeSCW8/k9KtP4I+f/yK3ZTatD9gbALP0lRi7/gQjA9ehJWA7GKvdGpVIIbIHaSIGSm9/7wZGHHv39i6WcNjkiFN7cMawk+r92qLhiUfXSi/gD631SgCl1PvAQEASeQOhzTJ06f3g/QwIoW3dUJn3o2wdd3pWgNj7sYVBx5jWZusA4VgrQnX1FQcTrrrNIzTbvrHSstLodsxBlR8O/QnEmLWjDLT/O3TJfRzTbwmtXnWxz75+/lzi4p0nW7LyVxeGoTh6pzuX+pK3Vw6vLXuaxd8sY9PqzXTs0YE2nfau9+uKhikeiXxvYOddZtcAUTP7lVJDgCEAbdokrh61AL31SgguYXvrNPgTunAQNJuOsuRHjjmOAR6O8Wonyhnjdt3avvoLhpbVMeI4cRwPvk+JdI9sY4Dt0F3XIbcdBv5vieo60oFI6VV8ZOXCYceUoRQ03zvIYX3LuOPijvS/8ipatmse//cSg1KKrkdXU91RNCkJm36otR6nte6hte6Rn5+fqMs2eTq4NFIsqmoXgw6gPTuWsivL3pB+DeAk8rGomCrpOh1s0TvJK2sHwB3jiqqa44mnMm4BozmobfG4QGWhsh7c9evc54CRBuw8BdMJRj47t9S3TZAxLOB0mzzysTUlCo2JxiceLfK1QOudft6n4phoCEKriP19HYDQ8kpHjPSr0Y6j0d7JoIMo18lg6xF75Z3jmEiyMz1VHtDgGY9OH5LUPSsBlCUP8qeD73N0cAlYOkT2sdzNRsTKyIK8SejSJ8A/O7KxsvsC8E5nVx9tK1KCVSRHPBL5PGB/pVR7Ip/yQcCFcTiviAfr/sTuK3aC7eCoo8p2EMp2UIznV3mesqPThkHpvUDV2Rt+8E0D97m1iTiulHKA6wyU64w9e52lJSp7TKVjZngNeJcT/X4rGDm1jFKIuqlz14rWOgQMA6YDy4AJWmvZr6mBULaOYO8B7Dy/2ADlQLnPq9u58VC5+2EbLzq0qk7nbohU2pWgouerRx50gftKdHgDOlyQ2MBEkxeXPnKt9TStdUetdQet9a47IEXCqZwXwH0RqEzAAY6+qLyPIitP68LaKbLIJuqCblR1W6ylMGVtj8p9G6xddzpqA5zgPAU876IL+qMLjsXcfGaDmU8vGj+lY045q189evTQ8+fPT/h1RXxpbaK3nAWhP9gxmGoFy16oZp83+h3qtVkG5ka0csPmU6ps5myAkYvK/1+j/3sQiaOUWqC17lH1uBTNErWmlIHKfQfc54PKBpUBrjNReR82ieSljPTI7B3fjBhbl5mgvZHBUiHqmdRaEXWijHRU5p2QeWeyQ0me8FqiyxUQWR0a3pjwcETTI4lcNAnr/tzAF6/PpmhTMb1O6s6Rp/XAYo1dqndPKfuhaO8HFTVddmYBW9eYrxEiniSRi0bvm4/nMvqSZwiHQoSCJrPHz2K/bq0YM+tRbPY41EVx9ANL6yqbiDjB3j3mYioh4i1l+sh1YAHm5jMxN3TC3NgLs+yFiiJQQlQv4Avw2BXP4/cGCAUjnxdfueb3n/7hy3G3xeUaStlQueMh7d9g7AOWtpB+LSpnnGxjJhIiJRK5Di5DFw6G0K9EBpGKoOxFdOlDyQ5NNHDLf/wDVPSCKL/XYNb7S+M2310Z6RgZN2I0n4WRPwMjfWiTGPAVDUNqJPKKQkWVecHzAdosTUZIIkXYnTa0Gb2lG4DDpcH/TYIjEiL+UiKRR2qCxJjvrmwVMwaEiK1jjw64M6KHgpzuMKdcXAIqufVghIiH1Ejk1gOIVNWrQgfBIjWYRfUMw+CBydeRkR3GnR7G6Q5jc5gMuKCQI04sA2f/ZIcoRJ2lxKwVlX4N2v8N4N3pqBNcZ6OMjGSFJVLEfj0O572VlzF/0mhKi210PcrLXm2DqOyxKCOzXq758+wlvDj8Tf7+9R9yWmRz4aizOGVIfxn8FPUiNRK5rTPkvoIuuT+yB6PKhLTLUGlXJzs0kSKc2QPpfdlxEPgBsILjSFR1BbBqSfumo8ueI+Rfi1loYNEtCAXdFKzZwkvD36K82MP5I/esCqMQNZFytVa01tKqEXGjza1o7xQIr0PZDwNHv9h7mu6G6fkASh5k26C8aULApxhx1n78/ktkYwt3houJBa/GZ+66aJIaTa0VSeKJpbVGaz/J+MKvbzq4GF3QD0ofB8/r6OKR6C1no6M2y9jNeXQYSh9j55lVhgF2p+aK23bsXxoOhSkuKIlX+EJsl3KJXCSG1hqz/D30piPRG7uhC46KtDobCa01uuhG0OVsT8DaA6GV6PJX9+xkZmHMDaoNA/Y/eMe4jjIUmc3qp09eNG0p0UcuEk97J0DpaLYPMJtboOQhTGwY7rOSGltchNdWU9DKD77JkPGfqEfmTf+ZKc9/QUlhGf8663BOGdofV5oTjCxizqoCNq6NdKM43A7OvulU7A7pVhHxJ4lcxFb2DJVnCRH5uexpaAyJfJf94NGPvfvARN4f/Qm+cj8Af/60ii9en83zPz6Mw+VAuy8Az3h27l7xew3efaIl6TlpnHfzQM4fOTDOb0KICEnkIorWGsxqtiszG0dZVmVpiba2j8yCqrTYzAmuynuNFm8u4d0HPyboD24/5vcG2LBqE1+++RWnXXUCKuNmNAZ43gNMUG4cLUZw99SzMCyGjO2IeiV95CKKUgqMahZaWVonNph6pLKfBiO3YnWnHXCBvScq7dJKz1v6w2/YHNFtHr/Hz5ypkdlXSlkxMm9BtZgX2RWo+fcY7nOxWC2SxEW9kxa5iC3jZii+lco1bpyojJHJiijulLU95H8V2cUnvAFsh4Cta1TizczLiDlrxzAUuS2zK59T2cGSV69xC1GVJHIRk+E6Ga1s6NInILwGLK1RGSNQzn7JDi2ulLKD88RdPufAI/YnMy8DX7kfbe5I6BabhSMH9qzvEONOh9aA73O09qGc/VC2g5IdkqijlFsQJEQyrPtzA7ef/BCb1xYS9AcxwyY2Z2QGSu+BPbnlrf9gtTXsdlF5iYei1W/SMm8sSplAGLCD+1xUxh3SBZQCGs2CICHqm/bNxiw4GXPDwZgFA9C+6bTq0JLXlz/NIccehDIiCS/oCxL0BflhynzeH/1JkqOuntaat+//kH93uoLc9KdQyg8EARPwgWciBBckOUpRF5LIhdiJ9v0XXXQ9hP8A/BBeiS66GdP7GeFQmIUzfiEcrLxRhd8bYOrY6ckJuAa+mvA9E8ZMpttRWwiHYrW6fWjvpwmPS8SPJHIRN1prvnhtFkO7j+CSDtcy9sbXKSooTnZYe0SXjiF6ExMflD5KMBAiHI69vaC3LHplZ0PxwaOTI/37mphl/SOLmSQVpDL51xNx88y1r/D89a+xctHfbFi1iakvfMnVh91CeXF5skOrufDq2MfNdTjddtoeFD39UhmKQ/t3refAaq9oY+TL9Mf/ZmJYY2VyB8oli5VSmSRyERcFa7Yw/fXZ21c+AoQCIUq3lDLtlf/G9Vpaa7T/B8yShzBLn0WHqkm+taGyYh83mqGUwU3jhuJMd24f2LQ5bKRnpzFkzCXxiyHODj2+K4bFoLzEwphhbfB7FT6PIuBXaByQdjnK3i3ZYYo6kEQu4uL3BStjL5rxBvh51pK4XUdrE110HbroKvC8AeUvoDefgumdWvdzh7eArqYryHUxAJ167c+4RY8xcNiJHNa/K+ffMpBXf32SVh1a1vn69eXSe87DnenCarPw3efZXHr4gbz+SBv+WTMIo9kUjIwbkx2iqKOGPV9KpIy8VjmYMfqPLVaDlvu2qPF5tNYQXBTZAMLIAedJKGOnVrJ/VsWGydvqwIQi/xWPQjuORRnptX8T/i8BG5FpeTsz2LnNs1f7Flz1+OW1v06CtWibz7hFjzPh0cn88tVSWrZvTt/LB7L/UQckOzQRJ5LIRVx07NGBlu1b8M/yNYRDOxK61W5l4LUDanSOSGv7BvB/BfgBB5Q+Ajkvo+w9I10qZc8CMeqFKwsE5oDz+Nq/Ce0nMiUvFn+ln7QOgm8a2jcNVBrKPQhl71X7a9ez/H3yuPbpwckOQ9QT6VoRcaGUYvSXd3BQ707YHFacbge5e2Vz98QRtOlUww2yfdMg8BWR1rYZ+b/2oLcOQ+sQuvRRCP1WXQSg6lgi1nEsscvR2lE7fUFoHUIXXoEuviuyvN/3Gbrw/zDLxtbt+kLUkrTIRdzktszh8dn3snVjEZ5SL3vt2wLDqHlbQXsngq5aOhcggPbPAc/bRHd77MR+5B7HvDNlbYtO+z8ofwUIEJmr5wT3OZWXsftnQGgJO7p3dOTPZWPRrnNRlvw6xSHEnqpTIldKnQvcAxwI9NJay7p7QU6LbHJaZO/+iXvC3BBpcWt/jAcVKntspG5KHRkZ16Gdx6K9U0GHUK5TInt57kT7ZkZ2E4pig8BccJ1a5ziE2BN1bZEvAc4CXopDLKKJU66z0MGfYrTKbeA4Gkruif1Ce1+U44j4xWE7GGU7eBdPyCLSK1mlP10BdRlsFaKW6tRHrrVeprVeEa9gRBPnPAXsfQAXkY+mE5QLlf0shqU5OAdEjlV+ESpjWELDVO5zidQvr8oG9qMSGosQkMA+cqXUEGAIQJs2bRJ1WZFClLJA9nMQ/CkyA8XIrph+mBN5POshtJEFng+BIFhaoTLv2XXruT7itB2IzrwTSu6rGGDVgAOV+2pcuneE2FO7LWOrlJoJxFrtMEprPbniOf8DRtS0j1zK2Iq60DoU6StX7qSWXtVmGQTmgXKjbV1Qvs/Q/llg5KPcF6BsnZMWm2icqitju9sWuda6DhNzhYg/pay72Tw5QXEY6eA8Fq29sOW8ilIBXsBAeyejM+/HcEsNk+qs+X0986f/jCvdSe8zepGenZbskFJW8n8bhEhxZvn7LJhZwLR3mhPwGRx75lb6DizCUno32nUiSlXt1xev3PoOk575HNAYFgvP/edV7vn4Zg7rLzVfaqOu0w/PBJ4F8oHPlFI/a613vW+W2K1QMMSCLxexdVMJXfp0Yp/990p2SI2CNovRZS+A73NQDnANQqVdGmnh18Ert03j09db4fNYAFg8J42ZH+bywHubsAYXgz31toOrT4u++pXJz31BwBeoOBIE4N6zH+PDja/gcDmSF1yKqtMnWGs9CZgUp1gE8PeyNdzc7x78ngCmaWKGTY6/+GhueGmobMVVB1r70FvOhvB6tiUOyp5CBxegcp6v9XnXr9rIlFcg4LdsP+bzWFi6wM28WS6OPL/hdhdosxzCf4OlJcrITdh1Z7z5P/ze6PUASikWzlzMkadFdQGL3ZAl+g2I1pq7B46maFMxnlIvvnI/AV+QWeO/Zfb73yU7vNTm/RTCBWxP4gD4wP8NOlj7GbQ/z1qCYYluD/nKLcydkQfWA2t97vqitcYsew696Uh04cXoTUdjbr0erROzOUYoGKa6ORah4C5W7opqSSJvQFYvW8OW9VujPuS+cj+fvvhlcoLaQ0UFxbxy2zsMOWQ4t530AAtmLEp2SADowDx2LKnfmYJg7cvspmenoSzRNV4sVk1mq5Ma5F2U9n4CZS8DPtBlQAD8s9DVLbiKs2Mv6IMzLbr7JBwKc+jxiZ1K2lhIIm9AAr7g9o19q/J5Yi1Nb1iKN5cw9JCb+fjJz1j1y2rmT1/E3Wc+yqRnpyU7NLC2IeYiHmWApfa1xHud3B3DEv1vZrU5OHHwGbU+b70qf5noLzU/eD+NzMCpZ71O6s5RA3tuT+ZWuwW7y86NL19FWqa73q/fGMmslQZk365tt+88szOHy86xg3onIaI9M/GJqZQWlhIMhLYf83v8vHrbewwY3A9XWvJmbyjXOejyl0EHdjpqidQ8r0OxLYfLwSNf3MEdpz5C0B8EBWbYZPgrVzfcQWpzSzUPKDDLwOKq18srpbj17ev49bvl/DB1Pu5MN/0u7MNe7Wtet15UJom8AbFYLdz69nXcd+5jhEMmoUAIZ7qT1h1bcfo1DX8y0LwvfiboD0Udt1gNVi1eTecjOiYhqghlaQE5b6CLR0B4I6DBdjAq+0mUqtuNaade+/PBunH8+v0KAr4gXfp0wuluwDMv7D3AP5OonZiNTDDyEhKCUooufQ6kS5+GN4aQiiSRNzC9TurOK0ue5IvXZrF5bSE9TuhGn7MOj9lSb2jyWuXw589/RR0PB8PktKhmL8wEUvZDoNkMMDeCssd1pobFaqHr0bVbyRlZIVoxmG3vXbddjmpAZdyEDnwP2seOssBOyLirzl9qIjkafnZoglq2a87l9w1Kdhh77Nzhp7Pof0vx79Sfb7VZ2K97+6TfNmut0Z4PwPMKmFvBdhhkjkRZ90tqXKZ3BhSPiOxwBKBD6KzHMFwn1Ns1lbUD5E1Gl78AgYVgaYNKvyqqXK9IHbuttVIfpNZK4zXlhem8fMs7GIYiFAjRsUcH7v5oBNn5yW2RmyWPVWxMsW0wT0VqteRNRlmTU8RNhzejC/oBVaf9OVH5/5UNKkSUWtdaEYn38+wlTHhsCpvXbOGwE7px3ojT479RQz05/eoTOfHyvvy15B+y8jNp2a55skOKdF143qTyvpsatA9d/iIq66HkBOabRlQ/NUSO+T6HtEsTHZFIUZLIG5hpr8xk7A1vbO+e+GfFOma+9RUvLXqM3JY5SY6uZhwuBwf0TG6XRSXhVdXsLhSGwM81Ps1Psxbz/uhP2PT3Zrr1PYgLbjuTFm3r0GrWXiB6cBhCoMtrf17R4GitwVwHWoNl77ivL5CRjQYk4A/y0vC3KvUxhwIhyorK+WDM5CRGluKMvapMO9xGgbVdjU4x/Y3Z3Hn6Iyyc8QtrflvHF6/NYmj3EWz4a1Pt43IcA8TaMNpW8VjqCYfD/DRrMbPe+4b1qzYmO5wGQQdXoDefhC4YEPn/5gHo4LK4XkMSeQOyetmamMdDwTDzvqh5y1FUpizNwHk8UHVKoAOVftVuXx8Khnjxpjfxe3Z8GYRDYbylPt6+98Oo52vtRXsnocvGov1fo7UZ9RwAZesErnNA7TRvW7nAdVZK1jJfv2ojl3YYxt1njuGpq8dx5UE38tTVL5GMcbiGQpvl6MKLIbySSNeeH8Kr0IWXRLr84kS6VhqQ7PxMQsFYt9qQt1dq9JE3VCprNFrdD97JgAlGs4rdhbru9rUb/iogFIquAWKGTX6eXXl5vw6tRG+5APBHpvcpJ1j2hdx3UEb0qkWVeSc4+6N9UwAT5RxYpwVKyXTPmY+yec0WTHNH4v7vO9/QpfeBHH/x0UmMLIn800EHo4/rIPi+APc5cbmMJPIGpNneeXQ+6gCWfLOsUvEgh9vBuSMSv0GBDv6C9kwCgijnSWA/qkHWDqkJpRyorAfQmXeB9oDKqva96PCGyOpHaweUcpKZl064mmJOuXtVHrfQRcNBF7F9EFN7IPQ7uvwlVMaNMeJS4DgS5UjN5L3N+pUbWfv7+kpJHCJ1gqaM/aLpJvLwRqJnJRE5Zsav60m6VhqYOyfcROejDsDutOHOdOFwOxj84CB6ndQ9oXGYZWPRWy4G73jwTkAXXYMuviXlb5OVsqOM7JhJXJvFmIWXoQv6R259Nx2BWf4umbkZHH7yodgclfuzHW4Hg245Y6fXF0LoN6JnovjB+0k9vJuGw1fuw7DETiee0sRUVWyQbIdE7sqqUq7IY3EiLfIGJjM3g8dn38vGvwvYurGItge1TniNEh1eB2UvUGm6nvZGbhOD5zbajRJ00XUQmA8Ed8xwKR2DtrZj5JvX8vDFzzD/y0Xs0z7EQb2K6XnyMRw5sKY72qT2F+DutOm8D3aXHW9Z5aRtd9roe15q323Uif0IsHauqLC57e/GCdZOce1CkxZ5A9WibT6deu2fnEJT/q/ZsXR7J9qL9s1IeDiJoMMbIqscqdqf6UWXv4wr3cW9k0YyceV+jJ25lGEPb+CIoz9Eb+qDDv4KEFnyb+0IVG3tO8DVQCshxonFYmHkG8NwuO1YbZFVqs40By3bN+esG05NcnTJo5RC5b4B6cMiYyWWfSH9WlTum3EthyAtchFFh9cQe34z22dYLPrqV964833+Wb6WfQ5oxRX3X0C3vgclLsh4M7dUM9ecin5OwD8bJ5NBVcxe2dYNvnUI5H+DUgYq+/GdBju9kb8vS3tU2u5nx6S6Xid1Z9yix/n0pRkU/LOZw/p3o9+FfbA7Y5QPbkKUsqPSh0D6kHq7hiRyEa2ihRmT/XDmTf+Ze896FL83ktCKN69g1KkPcffEEfQckNi+/LixdgBiTRO0geNfAJFaLbE2p9AeCC4Ce3eUdV9o/r/IjITwOrAdDPY+DaoY1bZxjvoYuG7VoSVDxlwS9/OKXWs4ny7RcJiF1TzgQKm0yJxqb+UFNn5PgBduerP+Y6snSjkh/WZg51rcNlDpqLT/i/xY7VZoqlJLXikXynUmKv1alOPoBpPEtVmEWTQCvfFg9MbOmIVDIuMhIuU1jE+YaFgc/yLmbjoYYN2ff1asjfmyNStSOykYaRdFNmK2HwWWDuC+CNVs6vbiVcp1GpUT/TYa7A37TkRrE114UUV9lwCR8gRfo7ecE9mEWaQ0SeQiikobHNlkoNLycRdk3IQy3GTnZ8Z8XVY1x1OJcvTByH0DI/9zjMzbUZadin65Bka6StS2hT1WwAmZj6BUA95IAiDwA4TXUnnswwSzHHyfJSsqESfSRy6iKCMX8qaiPa+C7yuwNEelDUZV9BUPuvVMXh81vtI+og63gwtuOzNZISeEUjbIfRP8s9H+r8DIRbnOTloZ3OU//s53n/yIzWHj2EG9aX3A3tU/OfQn6FgD2F50cEXUPBuRWiSRi5iUJQ+VMRIyRkY9duZ1J+Mt8/HBmE8wQyaGxeD8kQM587qTkxBpYillAefxKOfxSY3j+ete5fPXZhHwBjEsBh+MnszQxy7h9GsGxH6BdT9Q1hjFw9yRmi8ipcnGEqLWgoEgxZtLyWqWgc0eq4qfqA9Lf1jBLSfcj6+88lRJu9PG2yufj1nuWGsTveV0CK1ix1x5A4w8VLMZMevAiIanuo0lpI9c1JrNbqNZq9w9SuK/L1zJB2MmM+3lmZRujV/1t6bk64lzKlVi3MawGMz9bGHM1yhloHLfA9epRKpA2sBxLCpvoiTxRkC6VkRCmKbJmMuf59uP5xAKhrHZrbxw0xs8MPW21F5IlAQWmwVlKHS4yt20UrvcpFsZmais0ZA1up4jFIkmLXKREN9N+pHvJs3F7wkQDobxlfvxlfu55+xHqy3dK2Lrd0EfbPbohK3DJkecJhsoN0WSyEVCfPH67Kg+XYjU9P71+xVJiCh1dejWjovvOge704bdZceZ5sDusjPyzWFk5KQnOzyRBNK1IhJil4PqCRxv11qnbE31nQ265Uz6nt+bH6f9hNVupfcZPclqlvrz+EXtSCIXCXHCpcew+OulUa1ypRSdj+pYr9c2TZPxD0/ioyc/pXRrGe0Oas01T11B934H1+t161vLds05/ZoTkx2GaACka0UkxNHnHknPAd1xpjlQSmF32nC4HdzxwU31PnVx3M1vM/7hSZQWloGGv5b8w52nP8KKeX/U63WFSJQ6zSNXSj0KnEakeMOfwBVa66LdvU7mkTdNWmuW/vAbC2YsIiMnnb6DepPTPKter+kt83JO8ysJ+CpP11MKep50KA9+elu9Xl+IeKpuHnldu1ZmALdprUNKqdHAbcAtdTynaKSUUhx01AEcdNQBCbtmwZpCLDYjattEreHvX/9JWBxC1Kc6da1orb/UensBhznAPnUPSYj4yW+dhxmKrjOuFLQ/ODk1UoSIt3j2kQ8GPq/uQaXUEKXUfKXU/IKCgjheVojqudKcDBw2AIe7cnVCu8vOJXefm6SohIiv3XatKKVmAi1jPDRKaz254jmjiNTHfLe682itxwHjINJHXqtohaiFfz98EZnNMpj4+FRKtpSxb9c2XP3kFXQ8rEOyQxMiLupcNEspdTkwFDhOa+2pyWtksFMIIfZcvQx2KqUGACOBY2qaxIUQQsRXXfvInwMygBlKqZ+VUi/GISYhhBB7oE4tcq31fvEKRAghRO3Iyk4hhEhxksiFECLFSSIXNRIOh1m/cqPs6iNEAyTVD8VufTXhe54Z9gp+TwAzHKbngO6MfHMYaZmyRZgQDYG0yMUuLf1hBY8Ofp6SzaX4PX6C/hDzvviJ+899PNmhCSEqSCIXuzThsSkEvJUrBwb9IRZ/s4xNq6XUghANgSRysUsbVm0i1uJfq93K5nVbEx+QECKKJHKxS936HoTVbok6HgqEadtZil0K0RBIIhe7dO6I03GluzAsOz4qzjQHg249QwY7hWggZNaK2KVmrXJ5ceEY3r7vQxZ8+QtZzTM5b8RA+p5/VLJDE0JUkEQudqt5m3yGv3JNssMQQlRDulaEECLFSSIXQogUJ4lcCCFSnCRyIYRIcZLIhRAixUkiF0KIFFfnzZdrdVGlCoC/E37h+GsGbE52EAnQVN4nyHttjBrT+2yrtc6vejApibyxUErNj7WjdWPTVN4nyHttjJrC+5SuFSGESHGSJvZpsQAAAmBJREFUyIUQIsVJIq+bcckOIEGayvsEea+NUaN/n9JHLoQQKU5a5EIIkeIkkQshRIqTRF5HSqlHlVLLlVK/KKUmKaWykx1TfVBKnauU+lUpZSqlGuVULqXUAKXUCqXUH0qpW5MdT31RSr2mlNqklFqS7Fjqk1KqtVJqtlJqacVn9/pkx1RfJJHX3Qygi9a6K/AbcFuS46kvS4CzgK+THUh9UEpZgOeBk4DOwAVKqc7JjarevAEMSHYQCRAChmutOwNHANc21n9TSeR1pLX+UmsdqvhxDtAoN7LUWi/TWq9Idhz1qBfwh9Z6pdY6ALwPDExyTPVCa/01UJjsOOqb1nq91nphxZ9LgWXA3smNqn5IIo+vwcDnyQ5C1MrewD87/byGRvpL3xQppdoB3YG5yY2kfshWbzWglJoJtIzx0Cit9eSK54wiciv3biJji6eavE8hUo1SKh34CLhBa12S7HjqgyTyGtBaH7+rx5VSlwOnAsfpFJ6Yv7v32citBVrv9PM+FcdEClNK2Ygk8Xe11h8nO576Il0rdaSUGgCMBE7XWnuSHY+otXnA/kqp9v/fvh2aIAAEYBR+f7E5hzOIzWizWUxWZzEZLGbBKBjEAawWcQH3OINuoMdx8L4JXrmfg+OSDIAFcGrcpB8kCbAHnqWUTeuemhzy322BIXBNck+yax1UQ5J5khcwBs5JLq2b/un7YL0GLnwexY6llEfbqjqSHIAbMErySrJq3VTJBFgC0+/ZvCeZtY6qwS/6ktQ5b+SS1DmHXJI655BLUucccknqnEMuSZ1zyCWpcw65JHXuDWKJZZZL3mRtAAAAAElFTkSuQmCC\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 51,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-02-28T13:40:57.565Z",
          "iopub.execute_input": "2020-02-28T13:40:57.579Z",
          "iopub.status.idle": "2020-02-28T13:40:57.689Z",
          "shell.execute_reply": "2020-02-28T13:40:57.717Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# A sample toy binary classification dataset\n",
        "svm = sklearn.svm.LinearSVC( random_state = 0 )\n",
        "\n",
        "def tp(y_true, y_pred):\n",
        "    return sklearn.metrics.confusion_matrix(y_true, y_pred)[0, 0]\n",
        "def tn(y_true, y_pred):\n",
        "    return sklearn.metrics.confusion_matrix(y_true, y_pred)[0, 0]\n",
        "def fp(y_true, y_pred):\n",
        "    return sklearn.metrics.confusion_matrix(y_true, y_pred)[1, 0]\n",
        "def fn(y_true, y_pred):\n",
        "    return sklearn.metrics.confusion_matrix(y_true, y_pred)[0, 1]\n",
        "\n",
        "scoring = {\n",
        "    'tp' : sklearn.metrics.make_scorer( tp ),\n",
        "    'tn' : sklearn.metrics.make_scorer( tn ),\n",
        "    'fp' : sklearn.metrics.make_scorer( fp ),\n",
        "    'fn' : sklearn.metrics.make_scorer( fn ),\n",
        "}\n",
        "\n",
        "cv_results = sklearn.model_selection.cross_validate(\n",
        "    svm,\n",
        "    X, y,\n",
        "    scoring = scoring,\n",
        "    cv = 3\n",
        ")\n",
        "\n",
        "# Getting the test set true positive scores\n",
        "print( cv_results['test_tp'] )\n",
        "print( cv_results['test_tn'] )\n",
        "print( cv_results['test_fp'] )\n",
        "print( cv_results['test_fn'] )"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[12 13 15]\n",
            "[12 13 15]\n",
            "[1 3 7]\n",
            "[5 4 1]\n"
          ]
        }
      ],
      "execution_count": 62,
      "metadata": {
        "execution": {
          "iopub.status.busy": "2020-02-28T13:45:12.161Z",
          "iopub.execute_input": "2020-02-28T13:45:12.171Z",
          "iopub.status.idle": "2020-02-28T13:45:12.199Z",
          "shell.execute_reply": "2020-02-28T13:45:12.213Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results = sklearn.model_selection.cross_validate(\n",
        "    svm,\n",
        "    X, y,\n",
        "    cv = 3,\n",
        "    scoring = sklearn.metrics.make_scorer( tp ),\n",
        "    return_train_score = True\n",
        ")\n",
        "cv_results"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 63,
          "data": {
            "text/plain": [
              "{'fit_time': array([0.00184798, 0.00086808, 0.00096416]),\n",
              " 'score_time': array([0.00058293, 0.0004859 , 0.00040984]),\n",
              " 'test_score': array([12, 13, 15]),\n",
              " 'train_score': array([32, 33, 34])}"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 63,
      "metadata": {
        "execution": {
          "iopub.status.busy": "2020-02-28T13:45:13.745Z",
          "iopub.execute_input": "2020-02-28T13:45:13.755Z",
          "iopub.status.idle": "2020-02-28T13:45:13.776Z",
          "shell.execute_reply": "2020-02-28T13:45:13.785Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Бинарная классификация и метрики оценки точности бинарной классификации\n",
        "\n",
        "В случае бинарной классификации мы говорим о *положительном* (*positive*) классе и *отрицательном* (*negative*) классе, подразумевая под положительным классом интересующий нас класс.\n",
        "\n",
        "Правильность не является адекватным показателем прогностической способности (примеры с обнаружением рака, вычислением террористов и т.п.).\n",
        "Т.к. цена ошибки может быть очень разной."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Типы ошибок бинарной классификации\n",
        "\n",
        "<img src=\"https://bitbucket.org/despairr/ds-course-2018/raw/dbbafa803be8b1f01cc078ba3ba96d176b220669/intro-to-ml-images/1062.png\" height=\"200\">\n",
        "\n",
        "Типы ответов:\n",
        "* *Ложно-положительный* (*false positive*) (*ложное срабатывание*, *ложная тревога*) (*ошибка I рода* (*type I error*)):\n",
        "  * ошибочно посчитали искомым объектом:\n",
        "  * тест на рак положительный у здорового больного;\n",
        "  * сработало подозрение на террориста для мирного гражданина\n",
        "  * письмо попало в СПАМ\n",
        "* *Ложно-отрицательный* (*false negative*) (*пропуск события*) (*ошибка II рода* (*type II error*)):\n",
        "  * ошибочно пропустили искомый объект;\n",
        "  * тест на рак отрицательный у больного человека;\n",
        "  * пропустили террориста, посчитав его мирным\n",
        "  * СПАМ попал во \"входящие\"\n",
        "  \n",
        "В коммерческих проектах обоим видам ошибок можно присвоить определенные стоимости, которые позволяют измерить погрешность конкретного прогноза в денежном выражении, а не с точки зрения правильности."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Несбалансированные наборы данных\n",
        "\n",
        "Пример (помимо предыдущих): прогноз кликов по баннерам на сайтах. Примеров когда пользователь кликнул может быть порядка 1%. \"Правильность\" 99% (константный прогноз отсутствия клика) тут совершенно не подходит для задачи.\n",
        "\n",
        "Пример несбалансированного набора данных: поиск \"девяток\" в датасете \"цифры\".\n",
        "Все девятки объявляем положительным классом, а \"не девятки\" — отрицательным.\n",
        "\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Создаём набор данных \"угадай девятки\" — у нас только 10% девяток\n",
        "digits = sklearn.datasets.load_digits()\n",
        "y = digits.target == 9\n",
        "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n",
        "    digits.data, y, random_state = 0\n",
        ")\n",
        "y.shape, y[ y == True ].shape, y[ y == True ].shape[0] / y.shape[0]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 64,
          "data": {
            "text/plain": [
              "((1797,), (180,), 0.1001669449081803)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 64,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:47:22.277Z",
          "iopub.execute_input": "2020-02-28T13:47:22.288Z",
          "iopub.status.idle": "2020-02-28T13:47:22.340Z",
          "shell.execute_reply": "2020-02-28T13:47:22.350Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Классификатор \"наиболее частый класс\"\n",
        "import sklearn.dummy\n",
        "\n",
        "dummy_majority = sklearn.dummy.DummyClassifier( strategy = 'most_frequent' ).fit( X_train, y_train )\n",
        "\n",
        "pred_most_frequent = dummy_majority.predict( X_test )\n",
        "\n",
        "print(\"Уникальные спрогнозированные метки: {}\".format( np.unique(pred_most_frequent)) )\n",
        "print(\"Правильность на тестовом наборе: {:.2f}\".format( dummy_majority.score(X_test, y_test)) )"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Уникальные спрогнозированные метки: [False]\n",
            "Правильность на тестовом наборе: 0.90\n"
          ]
        }
      ],
      "execution_count": 66,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:48:12.503Z",
          "iopub.execute_input": "2020-02-28T13:48:12.514Z",
          "iopub.status.idle": "2020-02-28T13:48:12.535Z",
          "shell.execute_reply": "2020-02-28T13:48:12.546Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Случайный (dummy) классификатор\n",
        "dummy = sklearn.dummy.DummyClassifier( strategy = 'stratified' ).fit( X_train, y_train )\n",
        "\n",
        "pred_dummy = dummy.predict( X_test )\n",
        "\n",
        "print(\"Правильность dummy: {:.2f}\".format( dummy.score(X_test, y_test)) )"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Правильность dummy: 0.83\n"
          ]
        }
      ],
      "execution_count": 73,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:51:48.606Z",
          "iopub.execute_input": "2020-02-28T13:51:48.617Z",
          "iopub.status.idle": "2020-02-28T13:51:48.639Z",
          "shell.execute_reply": "2020-02-28T13:51:48.649Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Логистическая регрессия\n",
        "import sklearn.linear_model\n",
        "\n",
        "logreg = sklearn.linear_model.LogisticRegression(C=0.1, solver = 'liblinear').fit( X_train, y_train )\n",
        "\n",
        "pred_logreg = logreg.predict( X_test )\n",
        "\n",
        "print(\"правильность logreg: {:.2f}\".format(logreg.score(X_test, y_test)))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "правильность logreg: 0.98\n"
          ]
        }
      ],
      "execution_count": 79,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:56:35.666Z",
          "iopub.execute_input": "2020-02-28T13:56:35.676Z",
          "iopub.status.idle": "2020-02-28T13:56:35.698Z",
          "shell.execute_reply": "2020-02-28T13:56:35.708Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Вывод — \"правильность\" как единственная метрика не подходитдля оценки бинарного классификатора.\n",
        "\n",
        "Для несбалансированных наборов данных \"правильность\" также не является адекватной метрикой, позволяющей количественно оценить прогностическую способность модели.\n",
        "\n",
        "Объявив «девятку» положительным классом, мы можем рассмотреть элементы матрицы ошибок в терминах ложно положительных (false positive) и ложно отрицательных (false negative) примеров."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://bitbucket.org/despairr/ds-course-2018/raw/dbbafa803be8b1f01cc078ba3ba96d176b220669/intro-to-ml-images/1062.png\" height=\"200\">\n",
        "\n",
        "Матрицы ответов модели \"Наиболее часто встречающийся класс\":\n",
        "```\n",
        "[ 403 0 ]\n",
        "[ 47 0 ]\n",
        " ```\n",
        "Матрицы ответов случайной (dummy) модели:\n",
        "```\n",
        "[361 42]\n",
        "[ 43 4]\n",
        "```\n",
        "\n",
        "Матрицы ответов модели логистической регрессии:\n",
        "```\n",
        "[401 2]\n",
        "[ 8 39]\n",
        "```\n",
        "\n",
        "Как вычислить *правильность* по такой матрице:\n",
        "\n",
        "$ Accuracy = \\frac{ TP + TN  }{ TP + TN + FP + FN} $\n",
        "\n",
        "Другими словами, правильность — это количество верно классифицированных примеров (TP и TN), поделенное на общее количество примеров"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Точность (precision)\n",
        "\n",
        "$ Precision = \\frac{ TP }{ TP + FP } $\n",
        "\n",
        "Точность показывает, насколько можно доверять классификатору в случае срабатывания.<br>\n",
        "То есть сколько из предсказанных положительных примеров оказались действительно положительными.\n",
        "\n",
        "Таким образом, точность – это доля истинно положительных примеров от общего количества предсказанных положительных примеров.\n",
        "\n",
        "Точность используется в качестве показателя качества модели, когда цель состоит в том, чтобы снизить количество ложно положительных примеров.\n",
        "\n",
        "Например модель, которая должна спрогнозировать, будет ли эффективен новый лекарственный препарат при лечении болезни. Клинические испытания дороги и провести их лишь в случае высокой уверенности, что препарат действительно работает.\n",
        "\n",
        "Или нам хочется уменьшить количество СПАМа в inbox-е.\n",
        "\n",
        "Точность также известна как *прогностическая ценность положительного результата* (*positive predictive value*, *PPV*).\n",
        "\n",
        "\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Полнота (recall)\n",
        "\n",
        "$ Recall = \\frac{ TP }{ TP + FN } $\n",
        "\n",
        "*Полнота* (*recall*) показывает, на какой доле истинных объектов алгоритм срабатывает.<br>\n",
        "Другие названия *полноты*: *чувствительность* (*sensitivity*), *процент\n",
        "результативный ответов* или *хит-рейт* (*hit rate*) или *доля истинно положительных ответов* (*true positive rate*, *TPR*).\n",
        "\n",
        "Например, сколько террористов было обнаружено из числа фактических террористов или обнаружено больних СПИДом среди фактически больных.\n",
        "\n",
        "Полнота используется в качестве показателя качества модели, когда нам необходимо определить все положительные примеры, то есть, когда важно снизить количество ложно отрицательных примеров.\n",
        "\n",
        "Нужно искать компромисс между оптимизацией полноты и оптимизацией точности. Можно получить идеальную полноту, спрогнозировав все примеры как положительные — не будет\n",
        "никаких ложно отрицательных и истинно отрицательных примеров.\n",
        "Однако прогнозирование всех примеров как положительных, приведет к\n",
        "большому количеству ложно положительных примеров, и, следовательно, точность будет очень низкой."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Интегральные метрики на основе *точности* и *полноты*\n",
        "\n",
        "Хотя *точность* и *полнота* являются очень важными метриками, но сами по себе они не дадут вам полной картины.\n",
        "\n",
        "#### Арифметическое среднее\n",
        "\n",
        "Единая метрика может быть получена как арифметическое среднее точности и полноты:\n",
        "\n",
        "$ A = 1/2 \\cdot (precision + recall) $\n",
        "\n",
        "<img src=\"https://bitbucket.org/despairr/ds-course-2018/raw/443959e3b5e41168ceba4adcffbe1e9e9084f246/other-images/average-precision-recall.png\" style=\"height: 300px\">\n",
        "\n",
        "Пусть есть алгоритм, точность которого равна 10%, а полнота — 100%:\n",
        "precision = 0:1 recall = 1:\n",
        "Это может быть случай, когда в выборке всего 10% объектов класса +1, а алгоритм является константным\n",
        "и всегда возвращает +1. Очевидно, что этот алгоритм плохой, но введенная выше метрика для него равна\n",
        "A = 0:55.\n",
        "\n",
        "В свою очередь другой, гораздо более лучший алгоритм, с precision = 0:55 и recall = 55 также\n",
        "характеризуется A = 0:55.\n",
        "\n",
        "Ситуация, когда константный и разумный алгоритмы могут лежать на одной линии, является недопусти-\n",
        "мой, поэтому следует искать другой способ построения единой метрики.\n",
        "\n",
        "#### Минимум (P,R)\n",
        "\n",
        "Чтобы констрантный и разумный алгоритмы не лежали на одной линии уровня, можно рассматривать:\n",
        "\n",
        "$ M = min( precision, recall ) $\n",
        "\n",
        "<img src=\"https://bitbucket.org/despairr/ds-course-2018/raw/443959e3b5e41168ceba4adcffbe1e9e9084f246/other-images/min-precision-recall.png\" style=\"height: 300px\">\n",
        "\n",
        "Данный подход решает вышеупомянутую проблему, например:\n",
        "\n",
        "precision = 0.05; recall = 1 ⇒ M = 0.05<br>\n",
        "\n",
        "Но есть другой нюанс: два алгоритма, для которых точности одинаковы, но отличаются значения полноты,\n",
        "будут лежать на одной линии уровня M:\n",
        "\n",
        "precision = 0.4; recall = 0.5 ⇒ M = 0.4<br>\n",
        "precision = 0.4; recall = 0.9 ⇒ M = 0.4\n",
        "\n",
        "Такое тоже недопустимо, так как второй алгоритм существенно лучше первого.\n",
        "\n",
        "#### F-мера\n",
        "\n",
        "Одним из способов подытожить их является *F-мера* (*F-measure*), которая представляет собой\n",
        "гармоническое среднее точности и полноты:\n",
        "\n",
        "$ F = 2 \\cdot \\frac{ Accuracy \\cdot Racall }{ Accuracy + Racall } $\n",
        "\n",
        "Если необходимо отдать предпочтение точности или полноте, следует использовать расширенную F-меру, в которой есть параметр $ \\beta $:\n",
        "\n",
        "$ F = (1 + \\beta^2) \\cdot \\frac{ precision \\cdot recall }{ \\beta^2 \\cdot precision + recall } $\n",
        "\n",
        "![f-measure-samples](https://bitbucket.org/despairr/ds-course-2018/raw/c3f5df9d66d44fe424b4a4f0c0a0195f0ff9b0f7/other-images/f-measure-samples.png)\n",
        "\n",
        "*f-мера* действительно дает более лучшее представление о качестве модели, чем правильность. Однако, в отличие от *правильности*, ее труднее интерпретировать и объяснить."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('always')\n",
        "\n",
        "print(\"дамми:\")\n",
        "print(\" правильность: {:.2f}\".format( sklearn.metrics.accuracy_score(y_test, pred_dummy ) ))\n",
        "print(\" точность: {:.2f}\".format( sklearn.metrics.precision_score( y_test, pred_dummy )) )\n",
        "print(\" полнота: {:.2f}\".format( sklearn.metrics.recall_score( y_test, pred_dummy )) )\n",
        "print(\" f1-мера {:.2f}\".format( sklearn.metrics.f1_score( y_test, pred_dummy )) )\n",
        "print()\n",
        "print(\"LogisticRegression:\")\n",
        "print(\" правильность: {:.2f}\".format( sklearn.metrics.accuracy_score(y_test, pred_logreg ) ))\n",
        "print(\" точность: {:.2f}\".format( sklearn.metrics.precision_score(y_test, pred_logreg ) ))\n",
        "print(\" полнота: {:.2f}\".format( sklearn.metrics.recall_score(y_test, pred_logreg ) ))\n",
        "print(\" f1-мера {:.2f}\".format( sklearn.metrics.f1_score(y_test, pred_logreg ) ))\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "дамми:\n",
            " правильность: 0.80\n",
            " точность: 0.04\n",
            " полнота: 0.04\n",
            " f1-мера 0.04\n",
            "\n",
            "LogisticRegression:\n",
            " правильность: 0.98\n",
            " точность: 0.95\n",
            " полнота: 0.83\n",
            " f1-мера 0.89\n"
          ]
        }
      ],
      "execution_count": 81,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:56:57.727Z",
          "iopub.execute_input": "2020-02-28T13:56:57.738Z",
          "iopub.status.idle": "2020-02-28T13:56:57.763Z",
          "shell.execute_reply": "2020-02-28T13:56:57.774Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Если мы хотим получить более развернутый отчет о точности, полноте и f1-мере, можно воспользоваться удобной функцией `classification_report`, чтобы вычислить все три метрики сразу и распечатать их в привлекательном виде:"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# classification_report для pred_dummy\n",
        "print( sklearn.metrics.classification_report(\n",
        "    y_test, pred_dummy, target_names=[\"not nine\", \"nine\"]\n",
        ") )"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    not nine       0.89      0.89      0.89       403\n",
            "        nine       0.04      0.04      0.04        47\n",
            "\n",
            "   micro avg       0.80      0.80      0.80       450\n",
            "   macro avg       0.47      0.47      0.47       450\n",
            "weighted avg       0.80      0.80      0.80       450\n",
            "\n"
          ]
        }
      ],
      "execution_count": 83,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:57:23.173Z",
          "iopub.execute_input": "2020-02-28T13:57:23.183Z",
          "iopub.status.idle": "2020-02-28T13:57:23.205Z",
          "shell.execute_reply": "2020-02-28T13:57:23.217Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# classification_report для pred_logreg\n",
        "print( sklearn.metrics.classification_report(\n",
        "    y_test, pred_logreg, target_names=[\"not nine\", \"nine\"]\n",
        ") )"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    not nine       0.98      1.00      0.99       403\n",
            "        nine       0.95      0.83      0.89        47\n",
            "\n",
            "   micro avg       0.98      0.98      0.98       450\n",
            "   macro avg       0.97      0.91      0.94       450\n",
            "weighted avg       0.98      0.98      0.98       450\n",
            "\n"
          ]
        }
      ],
      "execution_count": 84,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-02-28T13:57:27.451Z",
          "iopub.execute_input": "2020-02-28T13:57:27.462Z",
          "iopub.status.idle": "2020-02-28T13:57:27.483Z",
          "shell.execute_reply": "2020-02-28T13:57:27.493Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Оценка принадлежности к классу, значения порога\n",
        "\n",
        "Многие алгоритмы бинарной классификации устроены следующим образом: сначала вычисляется некоторое вещественное число $ b(x) $, которое сравнивается с порогом $ t $.\n",
        "\n",
        "$ a(x) = [b(x) > t] $,\n",
        "\n",
        "где $ b(x) $ — оценка принадлежности \"положительному\" классу.\n",
        "\n",
        "Часто бывает необходимо оценить качество именно оценки принадлежности, а порог выбирается позже из соображений на точность или полноту.\n",
        "\n",
        "![threshold](https://cloudfront.romexsoft.com/wp-content/uploads/2017/07/Shema_forThreshold_RomexColours-1.jpg.webp)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Пусть рассматривается задачного кредитного скоринга и была построена некоторая функция b(x), которая оценивает вероятность возврата кредита клиентом x. Далее классификатор строится следующим образом:\n",
        "\n",
        "$ a(x) = [b(x) > 0.5] $\n",
        "\n",
        "При этом получилось, что точность (precision) равна 10%, а полнота (recall) — 70%. Это очень плохой алгоритм, так как 90% клиентов, которым будет выдан кредит, не вернут его.\n",
        "При этом не понятно, в чем дело: был плохо выбран порог или алгоритм не подходит для решения данной задачи. Именно для этого необходимо измерять качество самих оценок b(x).\n",
        "\n",
        "![binary-threshold](https://docs.aws.amazon.com/machine-learning/latest/dg/images/mlconcepts_image2.png)"
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        "### Кривая точности-полноты (PRC)\n",
        "\n",
        "*Precision-recall curce*\n",
        "\n",
        "![PRC-1](https://bitbucket.org/despairr/ds-course-2018/raw/c3f5df9d66d44fe424b4a4f0c0a0195f0ff9b0f7/intro-to-ml-images/1104.png)\n",
        "\n",
        "![PRC-2](https://bitbucket.org/despairr/ds-course-2018/raw/c3f5df9d66d44fe424b4a4f0c0a0195f0ff9b0f7/intro-to-ml-images/1105.png)\n",
        "\n",
        "В случае идеального классификатора, то есть если существует такой порог, что и точность, и полнота равны 100%, кривая будет проходить через точку (1; 1). Таким образом, чем ближе кривая пройдет к этой точке, тем лучше оценки. Площадь под этой кривой может быть хорошей мерой качества оценок принадлежности к классу 1. Такая метрика называется *AUC–PRC*, или площадь под PR-кривой.\n",
        "\n",
        "Требование, выдвигаемое к качеству модели (например, значение полноты должно быть 90%), часто называют  *рабочей точкой* (*operating point*). Фиксирование рабочей точки часто бывает полезно в контексте бизнеса, чтобы гарантировать определенный уровень качества клиентам или другим группам лиц внутри организации.\n",
        "\n",
        "Как правило, при разработке новой модели нет четкого представления о том, что будет рабочей точкой. По этой причине, а также для того, чтобы получить более полное представление о решаемой задаче, полезно сразу взглянуть на все возможные пороговые значения или все возможные соотношения точности и полноты для этих пороговых значений."
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        "### ROC-кривая\n",
        "\n",
        "Еще один инструмент, который обычно используется для анализа поведения классификаторов при различных пороговых значениях – это кривая *рабочей характеристики приемника* (*receiver operating characteristics curve*) или кратко *ROC-кривая* (*ROC curve*). Как и *кривая точности-полноты*, *ROC-кривая* позволяет рассмотреть все пороговые значения для данного классификатора, но вместо точности и полноты она показывает *долю ложно положительных примеров* (*false positive rate*, *FPR*) в сравнении с *долей истинно положительных примеров* (*true positive rate*).\n",
        "\n",
        "$ TPR = \\frac{ TP }{ TP + FN } $\n",
        "\n",
        "$ FPR = \\frac{ FP }{ FP + TN } $\n",
        "\n",
        "*Доля истинно положительных примеров* – это просто еще одно название *полноты*, тогда как *доля ложно положительных примеров* – это доля ложно положительных примеров от\n",
        "общего количества отрицательных примеров.\n",
        "\n",
        "ROC-кривая строится строится в осях\n",
        "* False Positive Rate* (ось X) и *True Positive Rate* (ось Y ), аналогично PR-кривой: постепенно рассматриваются случаи различных значений порогов и отмечаются точки на графике.\n",
        "\n",
        "![ROC-1](https://bitbucket.org/despairr/ds-course-2018/raw/c3f5df9d66d44fe424b4a4f0c0a0195f0ff9b0f7/intro-to-ml-images/1112.png)\n",
        "\n",
        "![ROC-1](https://bitbucket.org/despairr/ds-course-2018/raw/c3f5df9d66d44fe424b4a4f0c0a0195f0ff9b0f7/intro-to-ml-images/1113.png)\n",
        "\n",
        "Кривая стартует с точки (0; 0) и приходит в точку (1; 1). При этом, если существует идеальный классификатор, кривая должна пройти через точку (0; 1). Чем ближе кривая к этой точке, тем лучше будут оценки, а площадь под кривой будет характеризовать качество оценок принадлежности к первому классу. Такая метрика называется *AUC–ROC*,или площадь под ROC-кривой.\n",
        "\n",
        "В случае идеального алгоритма AUC-ROC = 1, а в случае худшего AUC - ROC = 1/2.\n",
        "Значение AUC-ROC имеет смысл вероятности того, что если были выбраны случайный положительный и\n",
        "случайный отрицаельный объекты выборки, положительный объект получит оценку принадлежности выше,\n",
        "чем отрицательный объект."
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