Machine learning (ML) has revolutionized the world of data science by automating the process of building models. Regression analysis is a common technique used in ML, and both Ridge and Lasso regression are popular variants. In this blog post, we'll compare the two methodologies and identify how they differ to help identify which one to choose in different scenarios.
Ridge Regression
Ridge regression is a technique used to analyze multiple regression data that suffer from collinearity. Collinearity occurs when two or more predictors of the dependent variable are highly correlated, which can cause instability in the model. Ridge regression combats this issue by adding a penalty term to the loss function.
The penalty term (L2 regularization) adds a weight to each of the coefficients of the regression. The goal of the addition is to minimize the RSS (Residual sum of squares) and the penalty term, which is the sum of squares of the coefficients.
The following are some of the advantages of Ridge regression:
- Improved model accuracy
- Essential for datasets with multiple multicollinear predictors
Lasso Regression
Lasso regression (Least Absolute Shrinkage and Selection Operator) is similar to Ridge regression, but instead of adding a penalty term to the loss function, it makes some of the coefficients equal zero. This attribute makes Lasso regression especially useful in feature selection problems.
When the penalty term is high, Lasso regression leads to fewer features in the model, and the features whose coefficients are zero are eliminated. The penalty term (L1 regularization) is the sum of absolute values of the coefficients.
The following are some of the advantages of Lasso regression:
- Eliminates less important features
- Overcomes overfitting
Comparison
The table below summarizes the differences between Ridge and Lasso regression:
| Ridge Regression | Lasso Regression | |
|---|---|---|
| Type | L2 regularization | L1 regularization |
| Key | Stabilizes parameters | Selects important features |
| Pros | - Offers improved model accuracy - Essential for multicollinear datasets |
- Can eliminate less important features - Useful in feature selection problems |
| Cons | - Doesn't remove any features from the model - Demands large sample size |
- Can lead to overfitting - Doesn't work in multicollinearity cases |
As the table highlights, both regression techniques have their own set of strengths and weaknesses, and the selection of one over the other largely depends on the dataset being used.
When to Use Ridge or Lasso Regression
The following are the main factors to consider when deciding between Ridge or Lasso regression:
- Feature Importance: If feature importance is a concern, Lasso regression is an excellent choice because it has built-in feature selection capabilities. On the other hand, Ridge regression may be used when less important features are not expected to impact the final outcome.
- Number of features: Lasso regression is particularly effective when the number of features is large, as it can eliminate the least important variables. But for few features, Ridge regression is a more suitable choice.
- Strongly correlated variables: If the dataset contains variables that are correlated with each other, Ridge regression should be the method selected as Lasso regression can only handle one variable at a time.
Conclusion
This blog post has compared Ridge and Lasso regression techniques and provided insight into scenarios when each method may be more useful. In summary, Ridge regression is better when dealing with multicollinear data and is particularly well-suited when large samples are available. Whereas, Lasso regression is a great method when feature selection is the goal and when the dataset contains a large number of features.