Review:
L2 Regularization (ridge Regression)
overall review score: 4.2
⭐⭐⭐⭐⭐
score is between 0 and 5
L2-regularization, also known as Ridge Regression, is a technique used in linear regression models to prevent overfitting by adding a penalty term proportional to the square of the magnitude of coefficients. This regularization encourages smaller coefficient values, leading to more stable and robust models, especially when dealing with multicollinearity or high-dimensional data.
Key Features
- Adds an L2 penalty term to the loss function to shrink coefficients towards zero
- Reduces model complexity and prevents overfitting
- Handles multicollinearity effectively
- Results in more stable and generalizable models
- Closed-form solution exists for linear problems
- Applicable in high-dimensional feature spaces
Pros
- Helps prevent overfitting by regularizing model coefficients
- Effective in high-dimensional datasets with many features
- Mathematically simple and computationally efficient for linear models
- Reduces variance of the estimates with minimal bias introduction
- Widely supported and well-understood method
Cons
- Does not perform feature selection; all features are included with shrunk coefficients
- Selecting the optimal regularization parameter requires cross-validation
- May not perform well if some features should be completely excluded
- Less effective when features are highly correlated compared to other regularization methods like Lasso