> ## Documentation Index
> Fetch the complete documentation index at: https://docs.clickml.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Ridge Regression component

> Train a linear regression model with L2 regularization that shrinks coefficients toward zero to reduce overfitting and handle multicollinearity.

**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.