Boruta feature selection
Boruta Feature Selection: Boruta is an all-relevant feature selection method based on Random Forest. It aims to identify every feature that has a meaningful impact on the target variable. The algorithm creates "shadow features" by shuffling original features' values. A Random Forest model is trained on both original and shadow features. Feature importance scores are calculated for all features. Real features are compared with the most important shadow feature. If a feature scores higher, it's marked important; if lower, unimportant. Uncertain features are re-evaluated over multiple iterations. Boruta is robust and captures complex feature interactions. However, it is computationally intensive, especially with large datasets. Feature selection by color: 🟢 WIS, HE, AFB, DC : Marked green — these are important features with higher importance than the shadowMax. 🔴 ANCC : Marked red — consistently less important than shadow features, so it’s unimportant . 🟡 shad...
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