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
Examples
6
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)
Examples
Reducing The Dimension With Principal Component Analysis
Chapter 4: Feature selection
Examples
Low-Variance Feature Removal
Chapter 5: Model selection
Examples
11
11
11
13
13
13
15
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
Examples
Ordinary Least Squares
Credits
20
20
20
22
About
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1
Chapter 1: Getting started with scikit-learn
Remarks
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-learn
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()
2
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