Bootstrap Aggregating

Caesar AI Atlas Definition

Bootstrap aggregating, or bagging, is an ensemble method that trains multiple models independently on bootstrapped samples of the training data. Their predictions are combined to reduce variance and improve robustness, especially for models that are sensitive to data variation.

Other Definitions

Bootstrap Aggregating Source

(Also bagging or bootstrapping.) A machine learning ensemble metaheuristic for primarily reducing variance (as opposed to bias), by training multiple models independently and averaging their predictions.

Related Terms

Bagging Variance Ensemble Training Data Dataset Data Set Or Dataset