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
).