BaggingRegressor

BaggingRegressor

A model type for constructing a bagging ensemble regressor, based on MLJScikitLearnInterface.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

BaggingRegressor = @load BaggingRegressor pkg=MLJScikitLearnInterface

Do model = BaggingRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in BaggingRegressor(estimator=...).

A Bagging regressor is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it.