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Ensemble learning
Ensemble learning combines predictions from multiple models into one, aiming for better and more stable performance than any single model alone.
Prerequisite: Random forest
What matters
Combining diverse models can cancel out individual models' distinct mistakes, which is why ensembles often outperform their best single member on its own.
Example
Averaging the predictions of a random forest, a gradient-boosted model, and a neural network is an ensemble approach combining three different underlying models.
Common misconception
An ensemble is not automatically better just for having more models; combining several weak or highly correlated models often provides little benefit.
Recall question
Why can combining several different models outperform the single best model among them?
Keywords and related concepts
ensemble learningmodel combinationaggregationrandom forest