Random Forest Regressor component - ClickML Studio

Random Forest Regressor averages the predictions of many decision trees trained on random data subsets. It handles non-linear relationships well and is robust to noisy data.

Configuration

Parameter	Description	Default
N Estimators	Number of trees in the forest.	`100`
Max Depth	Maximum depth of each tree. Leave empty for unlimited.	None
Min Samples Split	Minimum samples to split a node.	`2`
Min Samples Leaf	Minimum samples in a leaf node.	`1`
Random State	Seed for reproducibility.	`42`

Input / Output

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

When to use

A strong default for regression on tabular data. Does not require feature scaling. Works well out of the box without extensive tuning.