Ensemble Methods
Ensemble methods combine multiple learning algorithms to produce more accurate and robust predictions than individual models. They follow the 'wisdom of crowds' principle, where combining several weak learners forms a strong predictor.
Example: Consult several doctors for a diagnosis; their combined opinion is often more reliable. Common techniques include Random Forests and Gradient Boosting Machines.
Bagging (Bootstrap Aggregating): Trains multiple models on different random subsets of data (with replacement) to reduce variance and prevent overfitting. Imagine multiple weather forecasts whose average prediction is more reliable than any single forecast.
Boosting: Sequentially trains models where each new model focuses on examples previous models struggled with. It converts weak learners into strong ones by giving more weight to difficult examples. AdaBoost and XGBoost are popular implementations that have won many data science competitions.
Stacking: An advanced ensemble technique that uses a meta-model to combine the outputs of multiple base models. Base models generate predictions, and a meta-model learns the optimal combination of these predictions. Think of it as specialists providing reports that a manager then synthesizes to form a final decision.
Bayesian Model Averaging (BMA): A probabilistic approach that combines multiple models by weighting them according to their posterior probabilities. It accounts for model uncertainty rather than choosing a single best model. This is similar to a scientific committee where each member's vote is weighted by their expertise.