Scikit-learn

scikit-learn

#scikit-learn

Table of Contents

About

1

Chapter 1: Getting started with scikit-learn

2

Remarks

2

Examples

2

Installation of scikit-learn

2

Train a classifier with cross-validation

2

Creating pipelines

3

Interfaces and conventions:

4

Sample datasets

4

Chapter 2: Classification

6

Examples

6

Using Support Vector Machines

6

RandomForestClassifier

6

Analyzing Classification Reports

7

GradientBoostingClassifier

8

A Decision Tree

8

Classification using Logistic Regression

9

Chapter 3: Dimensionality reduction (Feature selection)

11

Examples

11

Reducing The Dimension With Principal Component Analysis

11

Chapter 4: Feature selection

13

Examples

13

Low-Variance Feature Removal

13

Chapter 5: Model selection

15

Examples

15

Cross-validation

15

K-Fold Cross Validation

15

K-Fold

16

ShuffleSplit

16

Chapter 6: Receiver Operating Characteristic (ROC)

17

Examples

17

Introduction to ROC and AUC

17

ROC-AUC score with overriding and cross validation

18

Chapter 7: Regression

20

Examples

20

Ordinary Least Squares

20

Credits

22

About

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1

Chapter 1: Getting started with scikit-learn

Remarks

scikit-learn is a general-purpose open-source library for data analysis written in python. It is based on other python libraries: NumPy, SciPy, and matplotlib scikit-learncontains a number of implementation for different popular algorithms of machine learning.

Examples

Installation of scikit-learn

The current stable version of scikit-learn requires: ? Python (>= 2.6 or >= 3.3), ? NumPy (>= 1.6.1), ? SciPy (>= 0.9).

For most installation pip python package manager can install python and all of its dependencies:

pip install scikit-learn

However for linux systems it is recommended to use conda package manager to avoid possible build processes

conda install scikit-learn

To check that you have scikit-learn, execute in shell:

python -c 'import sklearn; print(sklearn.__version__)'

Windows and Mac OSX Installation: Canopy and Anaconda both ship a recent version of scikit-learn, in addition to a large set of scientific python library for Windows, Mac OSX (also relevant for Linux).

Train a classifier with cross-validation

Using iris dataset:

import sklearn.datasets iris_dataset = sklearn.datasets.load_iris()



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