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<!DOCTYPE html>
<html>
<head>
<title>Introduction to scikit-learn: Machine Learning in Python</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
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<textarea id="source">title: Introduction to scikit-learn: Machine Learning in Python
use_katex: True
class: title-slide
# Introduction to scikit-learn: Machine Learning in Python
.larger[Thomas J. Fan]<br>
@thomasjpfan<br>
<a href="https://www.github.com/thomasjpfan" target="_blank"><span class="icon icon-github icon-left"></span></a>
<a href="https://www.twitter.com/thomasjpfan" target="_blank"><span class="icon icon-twitter"></span></a>
<a class="this-talk-link", href="https://github.com/thomasjpfan/ml-workshop-intro" target="_blank">
This workshop on Github: github.com/thomasjpfan/ml-workshop-intro</a>
???
## Links
- https://scikit-learn.org/stable/
- https://github.com/thomasjpfan/ml-workshop-intro
---
name: table-of-contents
class: title-slide, left
# Table of Contents
.g[
.g-6[
1. [Introduction to Machine Learning](#introduction)
1. [Supervised Learning](#supervised)
1. [Preprocessing](#preprocessing)
1. [Pipelines](#pipelines)
]
.g-6.g-center[
]
]
---
name: introduction
class: chapter-slide
# 1. Introduction to Machine Learning
.footnote-back[
[Back to Table of Contents](#table-of-contents)
]
---
class: chapter-slide
# What is machine learning?
---
class: middle
# Traditional programming
## Prediction
---
class: middle
# Machine Learning
## Training
## Prediction
---
class: center
# Amazon Recommendations
---
class: center
# Higgs Boson
.footnote[
[Machine Learning Wins the Higgs Challenge](https://atlas.cern/updates/atlas-news/machine-learning-wins-higgs-challenge)
]
---
.footnote[
[Link to Source](https://www.broadinstitute.org/news/deep-learning-model-assesses-quality-stored-blood)
]
---
class: middle
# Types of Machine Learning
- Unsupervised Learning
- Reinforcement Learning
- Supervised Learning
---
# Unsupervised Learning
.footnote[
[Link to Source](https://scikit-learn.org/dev/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py)
]
---
# Reinforcement Learning
---
# Reinforcement Learning
.footnote[
[Link to Source](https://arxiv.org/abs/1912.06680)
]
---
name: supervised
class: chapter-slide
# 2. Supervised Learning
.footnote-back[
[Back to Table of Contents](#table-of-contents)
]
---
# Supervised Learning
$$
(x_i, y_i) \propto p(x, y) \text{ i.i.d}
$$
- $p$ is an unknown joint distribution
- i.i.d means independent identically distributed
$$x_i \in \mathbb{R}^p$$
$$y_i \in \mathbb{R}$$
## Goal during training
$$f(x_i) \approx y_i$$
---
# Generalization
## Goal during training
$$f(x_i) \approx y_i$$
## Generalization
$$f(x) \approx y$$
For *non-training data*: $x$
---
class: middle
# Classification and Regression
.g[
.g-6[
## Classification
- target $y$ is discrete
- Does the patient have cancer?
]
.g-6[
## Regression
- target $y$ is continuous
- What is the price of the home?
]
]
---
# Data Representation
---
# Loading Datasets
## Random datasets
```py
from sklearn.datasets import make_classification
from sklearn.datasets import make_regression
```
## Sample datasets
```py
from sklearn.datasets import load_breast_cancer
from sklearn.datasets import load_diabetes
from sklearn.datasets import load_digits
from sklearn.datasets import load_iris
from sklearn.datasets import load_wine
```
## OpenML
```py
from sklearn.datasets import fetch_openml
```
---
# Splitting Training and Test Data
---
class: chapter-slide
# Notebook 📒!
## notebooks/01-loading-data.ipynb
---
# Supervised ML Workflow
---
# Supervised ML Workflow
---
class: chapter-slide
# Notebook 📓!
## notebooks/02-supervised-learning.ipynb
---
name: preprocessing
class: chapter-slide
# 3. Preprocessing
.footnote-back[
[Back to Table of Contents](#table-of-contents)
]
---
# Housing Dataset
---
# Feature Ranges
---
class: chapter-slide
# Notebook 📕!
## notebooks/02-preprocessing.ipynb
---
# KNN Scaling
---
# KNN Scaling Decision Boundary
---
# Scikit-Learn API
.center[
## `estimator.fit(X, [y])`
]
.g[
.g-6[
## `estimator.predict`
- Classification
- Regression
- Clustering
]
.g-6[
## `estimator.transform`
- Preprocessing
- Dimensionality reduction
- Feature selection
- Feature extraction
]
]
---
name: pipelines
class: chapter-slide
# 4. Pipelines
.footnote-back[
[Back to Table of Contents](#table-of-contents)
]
---
# Why Pipelines?
- Preprocessing must be fitted on training data only!
## Bad
```py
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.fit_transform(X_test)
```
## Good
```py
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
```
---
# Pipeline Example
## Before
```py
scaler = StandardScaler().fit(X_train)
X_train_scaled = scaler.transform(X_train)
est = Ridge().fit(X_train_scaled, y_train)
# Evaluate on training data
X_test_scaled = scaler.transform(X_test)
est.score(X_test_scaled, y_test)
```
## After
```py
*from sklearn.pipeline import make_pipeline
pipe = make_pipeline(StandardScaler(), Ridge())
pipe.fit(X_train, y_train)
pipe.score(X_test, y_test)
```
---
# Pipeline Overview
---
class: chapter-slide
# Notebook 📕!
## notebooks/04-pipelines.ipynb
---
# Whats next?
## Intermediate Machine Learning with scikit-learn
- Cross Validation
- Parameter Tuning
- Pandas interoperability
- Missing Values
## Other Workshops
- Intermediate Machine Learning with scikit-learn: <br> *Evaluation, Calibration, and Inspection*
- Advanced Machine Learning with scikit-learn: <br> *Text Data, Imbalanced Data, and Poisson Regression*
---
class: title-slide, left
.g.g-middle[
.g-7[
1. [Introduction to Machine Learning](#introduction)
2. [Supervised Learning](#supervised)
3. [Preprocessing](#preprocessing)
4. [Pipelines](#pipelines)
]
.g-5.center[
<br>
.larger[Thomas J. Fan]<br>
@thomasjpfan<br>
<a href="https://www.github.com/thomasjpfan" target="_blank"><span class="icon icon-github icon-left"></span></a>
<a href="https://www.twitter.com/thomasjpfan" target="_blank"><span class="icon icon-twitter"></span></a>
<a class="this-talk-link", href="https://github.com/thomasjpfan/ml-workshop-intro" target="_blank">
This workshop on Github: github.com/thomasjpfan/ml-workshop-intro</a>
]
]
</textarea>
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