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Gradient Boosting Regressor builds trees iteratively, where each tree corrects the residual errors of the current ensemble. It typically achieves the best accuracy among regression models in the toolbox.

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Configuration

ParameterDescriptionDefault
N EstimatorsNumber of boosting stages.100
Learning RateContribution of each tree. Lower = more conservative, more trees needed.0.1
Max DepthMaximum depth of individual trees.3
SubsampleFraction of samples used per tree. Below 1.0 adds stochasticity.1.0
Random StateSeed for reproducibility.42

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Input / Output

Type
InputX Train + Y Train
OutputTrained Model

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When to use

Best when accuracy is the priority and training time is acceptable. Does not require feature scaling.