Ensemble Learning

Key concepts:

• Bias/variance effects of bagging vs. boosting.
• Stacking/blending; meta-learners and leakage pitfalls.
• Calibration and uncertainty with ensembles.

Common interview checks:

• When RF vs. GBM; how to avoid overfitting in GBMs.
• Practical tuning strategies and defaults.