Vs (LASSO & Ridge)

Description

Both LASSO and Ridge prevent overfitting by penalizing coefficients but differ in approach.

• Ridge Regression adds an SSE penalty proportional to the square of coefficients, shrinking them but not setting them to zero. It’s useful for reducing the impact of irrelevant features while keeping all.
• LASSO penalizes the absolute value of coefficients, with high regularization setting some exactly to zero. This makes LASSO useful for feature selection.
• LASSO is better when few features matter, creating a simpler model.
• Ridge is better when most features are relevant, preserving them but reducing their impact.