Scikit-Learn - Tutorialspoint

Scikit-Learn

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Scikit-Learn

About the Tutorial

Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python.

It provides a selection of efficient tools for machine learning and statistical modeling

including classification, regression, clustering and dimensionality reduction via a

consistence interface in Python. This library, which is largely written in Python, is built

upon NumPy, SciPy and Matplotlib.

Audience

This tutorial will be useful for graduates, postgraduates, and research students who either

have an interest in this Machine Learning subject or have this subject as a part of their

curriculum. The reader can be a beginner or an advanced learner.

Prerequisites

The reader must have basic knowledge about Machine Learning. He/she should also be

aware about Python, NumPy, Scipy, Matplotlib. If you are new to any of these concepts,

we recommend you take up tutorials concerning these topics, before you dig further into

this tutorial.

Copyright & Disclaimer

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in this tutorial, please notify us at contact@

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Scikit-Learn

Table of Contents

About the Tutorial ........................................................................................................................................... ii

Audience .......................................................................................................................................................... ii

Prerequisites .................................................................................................................................................... ii

Copyright & Disclaimer .................................................................................................................................... ii

Table of Contents ........................................................................................................................................... iii

1.

Scikit-Learn ¡ª Introduction ...................................................................................................................... 1

What is Scikit-Learn (Sklearn)? ........................................................................................................................ 1

Origin of Scikit-Learn ....................................................................................................................................... 1

Community & contributors.............................................................................................................................. 1

Prerequisites .................................................................................................................................................... 2

Installation ....................................................................................................................................................... 2

Features ........................................................................................................................................................... 3

2.

Scikit-Learn ¨D Modelling Process ............................................................................................................. 4

Dataset Loading ............................................................................................................................................... 4

Splitting the dataset ........................................................................................................................................ 6

Train the Model ............................................................................................................................................... 7

Model Persistence ........................................................................................................................................... 8

Preprocessing the Data ................................................................................................................................... 9

Binarisation ...................................................................................................................................................... 9

Mean Removal................................................................................................................................................. 9

Scaling ............................................................................................................................................................ 10

Normalisation ................................................................................................................................................ 11

3.

Scikit-Learn ¡ª Data Representation ....................................................................................................... 13

Data as table .................................................................................................................................................. 13

Data as Feature Matrix .................................................................................................................................. 13

Data as Target array ...................................................................................................................................... 14

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4.

Scikit-Learn ¨D Estimator API ................................................................................................................... 16

What is Estimator API? .................................................................................................................................. 16

Use of Estimator API ...................................................................................................................................... 16

Guiding Principles .......................................................................................................................................... 17

Steps in using Estimator API .......................................................................................................................... 18

Supervised Learning Example ........................................................................................................................ 18

Unsupervised Learning Example ................................................................................................................... 23

5.

Scikit-Learn ¡ª Conventions .................................................................................................................... 26

Purpose of Conventions ................................................................................................................................ 26

Various Conventions ...................................................................................................................................... 26

6.

Scikit-Learn ¨D Linear Modeling .............................................................................................................. 31

Linear Regression .......................................................................................................................................... 32

Logistic Regression ........................................................................................................................................ 34

Ridge Regression ........................................................................................................................................... 37

Bayesian Ridge Regression ............................................................................................................................ 40

LASSO (Least Absolute Shrinkage and Selection Operator)........................................................................... 43

Multi-task LASSO ........................................................................................................................................... 45

Elastic-Net...................................................................................................................................................... 47

MultiTaskElasticNet ....................................................................................................................................... 51

7.

Scikit-Learn ¡ª Extended Linear Modeling ............................................................................................... 54

Introduction to Polynomial Features ............................................................................................................. 54

Streamlining using Pipeline tools .................................................................................................................. 55

8.

Scikit-Learn ¨D Stochastic Gradient Descent ............................................................................................ 57

SGD Classifier ................................................................................................................................................. 57

SGD Regressor ............................................................................................................................................... 61

Pros and Cons of SGD .................................................................................................................................... 63

9.

Scikit-Learn ¡ª Support Vector Machines (SVMs) .................................................................................... 64

Introduction ................................................................................................................................................... 64

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Classification of SVM ..................................................................................................................................... 65

SVC ................................................................................................................................................................. 65

NuSVC ............................................................................................................................................................ 69

LinearSVC ....................................................................................................................................................... 70

Regression with SVM ..................................................................................................................................... 71

SVR................................................................................................................................................................. 71

NuSVR ............................................................................................................................................................ 72

LinearSVR ....................................................................................................................................................... 73

10. Scikit-Learn ¨D Anomaly Detection .......................................................................................................... 75

Methods ........................................................................................................................................................ 75

Sklearn algorithms for Outlier Detection ...................................................................................................... 76

Fitting an elliptic envelop .............................................................................................................................. 76

Isolation Forest .............................................................................................................................................. 78

Local Outlier Factor ....................................................................................................................................... 80

One-Class SVM............................................................................................................................................... 82

11. Scikit-Learn ¡ª K-Nearest Neighbors (KNN) ............................................................................................. 84

Types of algorithms ....................................................................................................................................... 84

Choosing Nearest Neighbors Algorithm ........................................................................................................ 85

12. Scikit-Learn ¨D KNN Learning................................................................................................................... 87

Unsupervised KNN Learning .......................................................................................................................... 87

Supervised KNN Learning .............................................................................................................................. 91

KNeighborsClassifier ...................................................................................................................................... 91

RadiusNeighborsClassifier ............................................................................................................................. 97

Nearest Neighbor Regressor ......................................................................................................................... 99

KNeighborsRegressor .................................................................................................................................... 99

RadiusNeighborsRegressor .......................................................................................................................... 101

13. Scikit-Learn ¨D Classification with Na?ve Bayes ..................................................................................... 104

Gaussian Na?ve Bayes .................................................................................................................................. 105

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