Ensemble Method
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
Any technique that combines multiple machine learning models into one stronger predictor. The term is used interchangeably with ensemble learning.
What is an Ensemble Method?
An ensemble method is any machine learning technique that combines multiple models into a single, stronger predictor. The term means the same thing as ensemble learning; the two are used interchangeably, with “ensemble method” naming a specific technique and “ensemble learning” the approach as a whole.
This dictionary covers the concept in depth on the ensemble learning page, including its three main families: bagging, boosting, and stacking.
Example of an Ensemble Method
Random forest is the canonical ensemble method. It trains hundreds of decision trees, each on a different random sample of the data, and takes their majority vote as the prediction.
Any single tree in the forest overfits its own sample. The vote across all of them cancels those individual errors, so the ensemble predicts more accurately and more stably than its best member. XGBoost does the same job by the boosting route, building its trees in sequence instead of in parallel.
Related AI terms: Ensemble Learning · Random Forest · XGBoost · Decision Tree
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