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solegalli/feature-selection-for-machine-learning: Code repository for the online ...

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

solegalli/feature-selection-for-machine-learning

开源软件地址(OpenSource Url):

https://github.com/solegalli/feature-selection-for-machine-learning

开源编程语言(OpenSource Language):

Jupyter Notebook 100.0%

开源软件介绍(OpenSource Introduction):

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



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