Ensemble Optimization: Gradient Boosting and Random Forest Frameworks

Ensemble models combine weak decision trees to construct highly robust predictive models. While Random Forests utilize bootstrap aggregating (bagging) to reduce model variance, Gradient Boosting Machines (GBM) sequentially fit trees to pseudo-residuals to minimize bias. We analyze hyperparameter tuning, learning rates, subsampling, and comparative benchmarks against deep networks for tabular datasets.