Back to GalleryRidge Regression
Ridge Regression is a regularized linear model that adds an L2 penalty (sum of squared weights) to the Mean Squared Error loss. This shrinks coefficients toward zero but never forces them to exactly zero.
Key Parameters & Visual Influence:
• Alpha (Penalty): Controls regularization strength. As Alpha increases, the model coefficients shrink closer to zero, which flattens the regression slope. This reduces sensitivity to training noise, trading a small increase in bias for a large reduction in variance.
Dataset Studio
CSV format: Headers in first row, last column = label/target. All values must be numeric.
Example: x1, x2, label