SFS - MLXtend vs Sklearn | Train in Data

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Feature Selection for Machine Learning

Welcome

Introduction (4:03)
Course curriculum overview (3:33)
Course requirements (3:00)
Course aim (1:44)
How to approach this course
Refer a friend program

Course material

Course material (2:01)
Download Jupyter notebooks
Download datasets
Download presentations
Resources to learn machine learning skills

Feature selection

What is feature selection? (6:15)
Feature selection methods | Overview (6:19)
Filter methods (3:33)
Wrapper methods (5:42)
Embedded methods (3:52)
Moving Forward (4:05)
Open-source packages for feature selection (3:00)

Filter Methods | Basics

Constant, quasi constant, and duplicated features – Intro (4:02)
Constant features (7:53)
Quasi-constant features (7:07)
Duplicated features (5:23)
Install Feature-engine
Drop constant and quasi-constant with Feature-engine (4:20)
Drop duplicates with Feature-engine (5:23)

Filter methods | Correlation

Correlation - Intro (2:41)
Correlation Feature Selection (5:32)
Correlation procedures to select features (3:37)
Correlation | Notebook demo (11:49)
Basic methods plus Correlation pipeline
Correlation with Feature-engine (8:01)
Feature Selection Pipeline with Feature-engine (2:19)
Additional reading resources
Added Treat: A Movie We Recommend 🍿

Filter methods | Statistical measures

Statistical methods – Intro (3:25)
Mutual information (6:11)
Mutual information demo (4:39)
Chi-square test (16:15)
Chi-square | Demo (5:54)
Chi-square considerations (9:19)
Chi2 - calculating the expected frequencies (optional) (3:51)
Chi-square quiz
Anova (5:54)
Anova | Demo (6:10)
Select features based of p-values (10:32)
Basic methods + Correlation + Filter with stats pipeline

Filter Methods | Other methods and metrics

Filter Methods with other metrics (3:04)
Univariate model performance metrics (5:52)
Univariate model performance metrics | Demo (4:23)
KDD 2009: Select features by target mean encoding (6:39)
KDD 2009: Select features by mean encoding | Demo (6:59)
Univariate model performance with Feature-engine (4:54)
Target Mean Encoding Selection with Feature-engine (5:20)
Extra Treat: Our Reading Suggestion 📕

Wrapper methods

Wrapper methods – Intro (6:39)
MLXtend
Step forward feature selection (3:14)
SFS - MLXtend vs Sklearn (4:06)
Step forward feature selection | MLXtend (6:00)
Step forward feature selection | sklearn
Step backward feature selection (3:13)
Step backward feature selection | MLXtend (5:50)
Step backward feature selection | Sklearn
Exhaustive search (2:45)
Exhaustive search | Demo (3:37)

Embedded methods | Linear models

Regression Coefficients – Intro (4:05)
Selection by Logistic Regression Coefficients (6:41)
Selection by Linear Regression Coefficients (2:44)
Coefficients change with penalty (5:26)
Basic methods + Correlation + Embedded method using coefficients

Embedded methods – Lasso regularisation

Regularisation – Intro (5:39)
Lasso (6:39)
A note on SelectFromModel
Basic filter methods + LASSO pipeline

Embedded methods | Trees

Feature Selection by Tree importance | Intro (6:46)
Feature Selection by Tree importance | Demo (3:40)
Feature Selection by Tree importance | Recursively (5:04)
Feature selection with decision trees | review

Hybrid feature selection methods

Introduction to hybrid methods (1:50)
Feature Shuffling - Intro (2:41)
Shuffling features | Demo (8:41)
Recursive feature elimination - Intro (2:21)
Recursive feature elimination | Demo (5:42)
Recursive feature addition - Intro (2:06)
Recursive feature addition | Demo (2:55)
Feature Shuffling with Feature-engine (5:39)
Recursive feature elimination with Feature-engine (4:53)
Recursive feature addition with Feature-engine (3:22)

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Additional reading resources
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SFS - MLXtend vs Sklearn

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