Gradient Boosting Classifier component - ClickML Studio

Gradient Boosting builds trees one at a time, with each tree correcting the mistakes of the previous ensemble. It often achieves higher accuracy than Random Forest but requires more careful tuning.

Configuration

Parameter	Description	Default
N Estimators	Number of boosting stages (trees).	`100`
Learning Rate	Shrinks the contribution of each tree. Lower = more conservative, needs more trees.	`0.1`
Max Depth	Maximum depth of individual trees.	`3`
Subsample	Fraction of training samples used per tree. Values below 1.0 add randomness (stochastic gradient boosting).	`1.0`
Random State	Seed for reproducibility.	`42`

Input / Output

	Type
Input	`X Train` + `Y Train`
Output	Trained Model

When to use

Strong choice when accuracy is the primary goal. Slower to train than Random Forest. Does not require scaling.