Phase 6 · Feature Engineering & Model Evaluation
TopicsHandling Imbalanced Data
Part of the AI Engineer Roadmap.
Summary
Techniques (resampling, class weights, SMOTE) for datasets where one class vastly outnumbers another — critical for tasks like fraud detection and churn where the rare class matters most.
How to Learn This
- 1Practice oversampling (SMOTE) and undersampling on an imbalanced dataset.
- 2Learn to use `class_weight` parameters instead of resampling as a simpler first fix.
- 3Always evaluate imbalanced models with precision/recall/F1, never accuracy alone.
More topics in Feature Engineering & Model Evaluation
Feature Scaling & NormalizationOne-Hot & Label EncodingFeature SelectionHyperparameter 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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