Ensemble Methods

Cascade models route inputs to progressively more expensive models, but only one model makes the final decision at each stage.

Ensemble methods take the opposite approach: run multiple models on the same input and combine their predictions. The combined prediction is almost always better than any individual model because different models make different mistakes, and aggregation smooths those mistakes out.