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Dimensionality Reduction with Feature Selection

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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: Variance Based Feature Selection

Identifying High-Variance Features for Machine Learning Models
Adjusting Variance Threshold for Feature Selection
Feature Selection with Variance Threshold
Dimensionality Reduction with VarianceThreshold

Unit 2: Univariate Feature Selection

Feature Selection with Chi-Square Test on Iris Dataset
Feature Selection with Three Top Features from Iris Dataset
Feature Selection Using Chi-Square Test

Unit 3: Mutual Information Feature Selection

Feature Importance Analysis Using Mutual Information
Feature Selection Using Mutual Information
Feature Interaction Analysis Task
Feature Selection and Visualization with Mutual Information

Unit 4: Recursive Feature Elimination

Recursive Feature Elimination for Creditworthiness Prediction
Adjusting Recursive Feature Elimination for Top 3 Features
Recursive Feature Elimination with Decision Trees in R
Recursive Feature Elimination for Celestial Data Analysis

Unit 5: Feature Selection in R

Identifying Key Features in MPG Prediction
Feature Selection with Threshold Adjustment
Training a Linear Model with Feature Selection on `mtcars`

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