Review:
Lasso Regression
overall review score: 4.2
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score is between 0 and 5
Lasso regression is a type of linear regression that uses L1 regularization to penalize the absolute size of the coefficients, leading to feature selection and shrinkage of less important variables.
Key Features
- Feature selection
- Shrinkage of coefficients
- Regularization using L1 penalty
Pros
- Effective for high-dimensional data
- Handles multicollinearity well
- Automatic feature selection
Cons
- May not perform well with highly correlated features
- Sensitivity to outliers
- Difficult to interpret coefficients