Feature Selection Methods - Interactive Visualization

Feature selection reduces model complexity by removing features that add noise without information. Random Forest importance scores each feature by how much it reduces impurity across all trees. Lasso (L1 regularization) shrinks unimportant feature coefficients to exactly zero. Mutual Information measures how much knowing a feature reduces uncertainty about the target - works for non-linear relationships. Recursive Feature Elimination (RFE) iteratively removes the least important feature and re-trains until a target count is reached. Removing correlated features eliminates redundancy without losing predictive power.

• Switch between Random Forest, Lasso, Mutual Information, and RFE to see how each method ranks the same 10 features differently
• Adjust the top-K threshold slider to keep 1–10 features and see which ones cross the cutoff
• Enable correlated feature removal to automatically drop features with correlation above 0.8 (shown in the heatmap)
• See the correlation heatmap highlight highly correlated feature pairs - income and credit score share 0.72 correlation in this dataset

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.