GradientBoostingClassifier

GradientBoostingClassifier

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

From MLJ, the type can be imported using

GradientBoostingClassifier = @load GradientBoostingClassifier pkg=MLJScikitLearnInterface

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