Highly Correlated Variables in Random Forests

Requirements Clarification & Assessment

1. Understanding the Question:

   • The question seeks to understand the impact of highly correlated variables on feature importance in a Random Forest model.
   • The focus is on how correlation affects the calculation of feature importance and not the prediction accuracy or performance.

2. Key Concepts:

   • Random Forest: An ensemble learning method that builds multiple decision trees and merges them to get a more accurate and stable prediction.
   • Feature Importance: A metric that indicates the significance of each feature in predicting the target variable. In Random Forests, feature importance can be calculated using impurity reduction or permutation importance.
   • Correlation: A statistical measure that expresses the extent to which two variables are linearly related.

3. Assumptions:

   • The reader has a basic understanding of Random Forests and feature importance.
   • The variables in question are continuous and have a linear relationship.

4. Objective:

   • To provide a comprehensive explanation of how highly correlated variables affect feature importance in Random Forests, considering different scenarios and methodologies.