Phase 6 · Feature Engineering & Model Evaluation
TopicsFeature Selection
Part of the AI Engineer Roadmap.
Summary
Choosing the subset of features that actually improve model performance — reduces overfitting, training time, and improves interpretability.
How to Learn This
- 1Practice filter methods (correlation, chi-square) and wrapper methods (RFE).
- 2Use feature importance from a tree-based model to prune weak features.
- 3Compare model performance with and without feature selection on the same dataset.
More topics in Feature Engineering & Model Evaluation
Feature Scaling & NormalizationOne-Hot & Label EncodingHandling Imbalanced DataHyperparameter Tuning (Grid/Random Search)Ensemble Methods (Bagging, Boosting)XGBoost & LightGBMEvaluation Metrics (Accuracy, Precision, Recall, F1)ROC-AUCConfusion MatrixBias-Variance TradeoffOverfitting & Regularization (L1/L2)
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