Ensemble Methods

Bagging, boosting, random forests, and model ensembles.

Sequentially training weak learners that focus on previously misclassified examples – boosting accuracy through reweighting.

Training multiple models on bootstrapped samples and averaging predictions – reducing variance through diversity.

Building an additive model by fitting each new tree to the residual errors of the ensemble – the most powerful tabular method.

Bagged decision trees with random feature subsets – robust, parallelizable, and hard to overfit with more trees.

Training a meta-learner on base model predictions – combining diverse model families for competition-winning performance.

Industrial-strength gradient boosting implementations with regularization, histogram binning, and GPU acceleration.