/feature-selection-for-machine-learning

Code repository for the online course Feature Selection for Machine Learning

Primary LanguageJupyter NotebookOtherNOASSERTION

PythonVersion License https://github.com/solegalli/feature-selection-for-machine-learning/blob/master/LICENSE Sponsorship https://www.trainindata.com/

Feature Selection for Machine Learning - Code Repository

Published February, 2018

Actively maintained.

Links

Table of Contents

  1. Basic Selection Methods

    1. Removing Constant Features
    2. Removing Quasi-Constant Features
    3. Removing Duplicated Features

  2. Correlation Feature Selection

    1. Removing Correlated Features
    2. Basic Selection Methods + Correlation - Pipeline

  3. Filter Methods: Univariate Statistical Methods

    1. Mutual Information
    2. Chi-square distribution
    3. Anova
    4. Basic Selection Methods + Statistical Methods - Pipeline

  4. Filter Methods: Other Methods and Metrics

    1. Univariate roc-auc, mse, etc
    2. Method used in a KDD competition - 2009

  5. Wrapper Methods

    1. Step Forward Feature Selection
    2. Step Backward Feature Selection
    3. Exhaustive Feature Selection

  6. Embedded Methods: Linear Model Coefficients

    1. Logistic Regression Coefficients
    2. Linear Regression Coefficients
    3. Effect of Regularization on Coefficients
    4. Basic Selection Methods + Correlation + Embedded - Pipeline

  7. Embedded Methods: Lasso

    1. Lasso
    2. Basic Selection Methods + Correlation + Lasso - Pipeline

  8. Embedded Methods: Tree Importance

    1. Random Forest derived Feature Importance
    2. Tree importance + Recursive Feature Elimination
    3. Basic Selection Methods + Correlation + Tree importance - Pipeline

  9. Hybrid Feature Selection Methods

    1. Feature Shuffling
    2. Recursive Feature Elimination
    3. Recursive Feature Addition