Review:
Ridge Regression (l2 Regularization)
overall review score: 4.2
⭐⭐⭐⭐⭐
score is between 0 and 5
Ridge regression, also known as L2-regularization, is a linear regression technique that introduces a penalty term to the ordinary least squares cost function. This penalty shrinks the coefficients towards zero, helping to prevent overfitting and improve model stability, especially in situations with multicollinearity or when the number of predictors exceeds the number of observations.
Key Features
- Incorporates an L2 penalty term (squared magnitude of coefficients) into the loss function
- Helps reduce model complexity and multicollinearity issues
- Produces unique solutions even in cases with correlated predictors
- Adjustable regularization parameter (lambda) controls the amount of shrinkage
- Widely used in high-dimensional data scenarios
Pros
- Effective at preventing overfitting in complex models
- Stable and unique solution for coefficients even with multicollinearity
- Simple to implement and interpret
- Can improve predictive performance on unseen data
Cons
- Introduces bias into estimates due to shrinkage
- Choice of regularization parameter requires tuning, often via cross-validation
- Does not perform feature selection; all variables are retained regardless of importance
- May underfit if regularization is too strong