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### Slide: Recursive Feature Selection in Random Forest **Title:** Recursive Feature Selection Technique **Overview:** - Recursive Feature Selection (RFS) is used to improve model accuracy by selecting the most relevant features. **Key Concepts:** 1. **Feature Importance:** - RFS ranks features based on their importance to the model's predictions. - Helps in identifying and retaining the most impactful features. 2. **Iterative Process:** - Features are recursively removed, and the model is re-evaluated to determine the optimal subset of features. - This process continues until the best performing set of features is found. 3. **Model Simplification:** - By eliminating irrelevant or less important features, RFS simplifies the model. - Reduces overfitting and improves model generalizability. **Impact on Backtest Performance:** - **Before RFS:** - Absolute Average Prediction Error: 1.51 bp - **After RFS:** - Absolute Average Prediction Error: 1.19 bp - This impr