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.Documentation Index
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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 |