Ensemble meta-estimators

The bagging classifier and the bagging regressor are ensemble meta-estimators that fit the base classifier and regressor models respectively on random subsets of the original dataset. The predictions from each model are combined to create the final prediction. These kinds of meta-estimators induce randomization into the model-building process and aggregate the outcome. The aggregation averages over the iterations for a numerical target variable and performs a plurality vote in order to reach a categorical outcome.

Bagging classifiers

Bagging classifiers train each classifier model on a random subset of the original training set and aggregate the predictions, then perform a plurality voting for a categorical...