HistGradientBoostingClassifier
HistGradientBoostingClassifier
A model type for constructing a hist gradient boosting classifier, based on MLJScikitLearnInterface.jl, and implementing the MLJ model interface.
From MLJ, the type can be imported using
HistGradientBoostingClassifier = @load HistGradientBoostingClassifier pkg=MLJScikitLearnInterface
Do model = HistGradientBoostingClassifier()
to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in HistGradientBoostingClassifier(loss=...)
.
This algorithm builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage n_classes_
regression trees are fit on the negative gradient of the loss function, e.g. binary or multiclass log loss. Binary classification is a special case where only a single regression tree is induced.
HistGradientBoostingClassifier
is a much faster variant of this algorithm for intermediate datasets (n_samples >= 10_000
).