mvmukesh / featureselection-framework-ml

Answer to how to select variables in data set and build simpler, faster, more reliable and interpretable ML models

FeatureSelection-Framework-ML

Answer of how to select variables in data set and build simpler, faster, more reliable and interpretable machine learning models

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Why Do we Select Features?

• Easier to implement by software developers --> Model Production
• Enhance generalisation by reducing overfitting
• Reduced risk of data errors during model use
• Simple model are easier to interpret
• Short training time
• Data redundancy

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Why?? Reducing Features for Model Deployment

• Smaller json messages sent over to the model
  • Json messages contain only necessary variables / inputs
• Less lines of code for error handling
  • Error handlers need to be written for each variable / input
• Less feature engineering code
• Less information to log

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How to make Features selection part of Pipeline ????

Feature Selection can be the part of Pipeline, but it is good to select Feature ahead before building pipeline and make the list of selected features part of the pipeline we want to deploy.

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Feature Selection Method	Nature	Pros	Cons
Filter Methods	Independent of ML Algorithm Based only on variable characteristics	Quick Feature Removal Model Agnostic Fast Computation	Does not capture redundancy Does not capture feature interaction Poor model performance
Wrapper Methods / Greedy Algorithms	Consider ML Algorithm Evaluates subsets/grop of Features	Considers feature interaction Best performance Best feature subset for a given algorithm	Not model agnostic(features they find may not be best for certain algorithm) Computation expensive Often impracticable
Embedded Methods	Feature selection during training of ML algorithm	Good model performance Capture feature interaction Better than Filter Faster than Wrapper	Not model agnostic

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1. Feature Selection Methods

• Filter Methods
  • Variance
  • Correlation
  • Univariate Selection
• Wrapper Methods
  • Forward Feature Selection
  • Backword Feature Elemenation
  • Exaustive Search
• Embedded / Hybrid Methods
  • LASSO
  • Tree Importance
• Moving Forward

Feature Selection Methods	Code + Blog Link	Video Link

2. Feature Selection -- Basic Methods

• Removing
  • Constant Features
  • Quasi-Constant Features
  • Duplicated Features

Feature Selection -- Basic Methods	Code + Blog Link	Video Link

3. Feature Selection -- Correlation

• Removing Correlated Features
• Basic Selection Methods + Correlation -> Pipeline

Feature Selection -- Correlation	Code + Blog Link	Video Link

Filter Methods

4. Univariate Statistical Methods

• Mutual Information
• Chi-square distribution
• Anova
• Basic Selection Methods + Statistical Methods -> Pipeline

Univariate Statistical Methods -- Filter Method	Code + Blog Link	Video Link

5. Other Methods and Metrics

• Univariate ROC-AUC, MSE etc
• Method used in a KDD competition - 2009

Wrapper Methods

6. Wrapper Methods

• Forward Feature Selection
• Backward Feature Selection
• Exhaustive Feature Selection

Wrapper Methods -- Feature Selection	Code + Blog Link	Video Link

Embedded Methods

7. Linear Model Coefficients

• Logistic Regression Coefficients
• Linear Regression Coefficients
• Effect of Regularization on Coefficients
• Basic Selection Methods + Correlation + Embedded -> Pipeline

Linear Model Coefficients	Code + Blog Link	Video Link

8. Lasso

• Lasso
• Basic Selection Methods + Correlation + Lasso -> Pipeline

Lasso	Code + Blog Link	Video Link

9. Tree Importance

• Random Forest derived Feature Importance
• Tree importance + Recursive Feature Elimination
• Basic Selection Methods + Correlation + Tree importance -> Pipeline

Tree Importance	Code + Blog Link	Video Link

Hybrid Methods

10. Hybrid Methods

• Feature Shuffling
• Recursive Feature Elimination
• Recursive Feature Addition

Hybrid Methods	Code + Blog Link	Video Link