Ridge Regression component - ClickML Studio

Ridge Regression adds a penalty proportional to the square of the coefficient magnitudes (L2 regularization). This shrinks all coefficients toward zero, which helps when many features are correlated or the dataset is small.

Configuration

Parameter	Description	Default
Alpha	Regularization strength. Higher = stronger shrinkage. `0` is equivalent to standard Linear Regression.	`1.0`
Fit Intercept	Whether to include a bias/intercept term.	`true`
Random State	Seed for reproducibility.	`42`

Input / Output

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

When to use

Preferred over plain Linear Regression when you have many features, multicollinearity, or a small dataset. Does not perform feature selection — all features get non-zero coefficients.