Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Dimensionality Reduction with Feature Selection

Go to class Write review

Overview

In this course, you'll learn specialized techniques for feature selection and extraction to improve machine learning models. Through practical applications on a synthetic dataset, you'll discover how to identify and remove low-variance features, use correlation with the target variable, and apply advanced selection methods to refine your datasets for optimal efficiency and effectiveness.

Syllabus

Unit 1: Mastering Variance-Based Feature Selection with VarianceThreshold in Python

Unveiling High Variance Features in Synthetic Data
Adjusting the Variance Threshold
Setting the Variance Threshold
Cosmic Code Crafting: Feature Selection with Variance Threshold

Unit 2: Unveiling the Power of Univariate Feature Selection with SelectKBest in Python

Unveiling the Most Informative Features with Chi-Square Test
Expanding Our Feature Universe
Uncovering the Stars: Selecting Features with Chi-Square
Implementing SelectKBest for Feature Selection

Unit 3: Mastering Feature Selection with Mutual Information in Python

Visualizing Wine Data with Mutual Information
Refining Feature Selection with SelectPercentile
Computing Mutual Relationships in Features
Wine Dataset Feature Selection with Mutual Information

Unit 4: Mastering Feature Selection with Recursive Feature Elimination in Python

Unveiling the Top Features with Recursive Feature Elimination
Adjusting Feature Selection with RFE
Navigating the Stars of Feature Selection
Navigating the Stars: Recursive Feature Elimination

Unit 5: Mastering Feature Selection with SelectFromModel in Scikit-learn

Revealing Key Features in California Housing Prices
Adjusting Feature Selection Threshold
Implanting SelectFromModel in the Voyage of Feature Selection

Reviews

Start your review of Dimensionality Reduction with Feature Selection