Phase 6 · Feature Engineering & Model Evaluation
TopicsXGBoost & LightGBM
Part of the AI Engineer Roadmap.
Summary
Industry-standard gradient boosting libraries — usually the strongest models on structured/tabular data and a staple of ML competitions and production systems.
How to Learn This
- 1Train an XGBoost or LightGBM model and compare it against scikit-learn's GradientBoosting.
- 2Learn key hyperparameters: learning rate, max depth, n_estimators, early stopping rounds.
- 3Use built-in feature importance to interpret a trained boosted model.
Previous
Ensemble Methods (Bagging, Boosting)
Next
Evaluation Metrics (Accuracy, Precision, Recall, F1)
More topics in Feature Engineering & Model Evaluation
Feature Scaling & NormalizationOne-Hot & Label EncodingFeature SelectionHandling Imbalanced DataHyperparameter Tuning (Grid/Random Search)Ensemble Methods (Bagging, Boosting)Evaluation Metrics (Accuracy, Precision, Recall, F1)ROC-AUCConfusion MatrixBias-Variance TradeoffOverfitting & Regularization (L1/L2)
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