HistGradientBoostingRegressor

HistGradientBoostingRegressor

A model type for constructing a gradient boosting ensemble regression, based on MLJScikitLearnInterface.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

HistGradientBoostingRegressor = @load HistGradientBoostingRegressor pkg=MLJScikitLearnInterface

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

This estimator builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function.

HistGradientBoostingRegressor is a much faster variant of this algorithm for intermediate datasets (n_samples >= 10_000).