Random Forest Classifier component - ClickML Studio

Random Forest trains many decision trees on random subsets of your data and combines their votes into a final prediction. It handles mixed feature types well, is resistant to overfitting, and requires minimal tuning.

Configuration

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

Input / Output

	Type
Input	`X Train` + `Y Train` (from Train-Test Split)
Output	Trained Model

When to use

A good default choice for most classification problems. Works well on tabular data without feature scaling.