Back to GalleryLasso Regression
Lasso Regression adds an L1 penalty (sum of absolute weights) to the MSE loss. This type of regularization has a unique mathematical property: it can drive coefficients exactly to zero, performing automatic feature selection.
Key Parameters & Visual Influence:
• Alpha (Penalty): Higher values force more coefficients to zero, resulting in a simpler, sparser model. In the visualizer, a high Alpha can cause the regression slope to become flat (zero slope) if the feature is deemed unimportant.
Dataset Studio
CSV format: Headers in first row, last column = label/target. All values must be numeric.
Example: x1, x2, label