Ensemble Methods (Bagging, Boosting)

Summary

Combining multiple weaker models into a stronger one — bagging trains models in parallel on random subsets (Random Forest), boosting trains sequentially, each correcting the last (XGBoost).

How to Learn This

1Compare a bagging model (Random Forest) and a boosting model (XGBoost) on the same dataset.
2Learn conceptually why boosting often outperforms bagging on tabular data.
3Understand the added risk of overfitting with boosting and how early stopping mitigates it.

Hyperparameter Tuning (Grid/Random Search)

XGBoost & LightGBM

More topics in Feature Engineering & Model Evaluation

Feature Scaling & Normalization One-Hot & Label Encoding Feature Selection Handling Imbalanced Data Hyperparameter Tuning (Grid/Random Search)XGBoost & LightGBM Evaluation Metrics (Accuracy, Precision, Recall, F1)ROC-AUC Confusion Matrix Bias-Variance Tradeoff Overfitting & Regularization (L1/L2)

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