Advantages of Lasso Regression
- Feature Selection: Lasso regression eliminates the need to manually select the most relevant features, hence, the developed regression model becomes simpler and more explainable.
- Regularization: Lasso constrains large coefficients, so a less biased model is generated, which is robust and general in its predictions.
- Interpretability: With lasso, models are often sparsity induced, therefore, they are easier to interpret and explain, which is essential in fields like health care and finance.
- Handles Large Feature Spaces: Lasso lends itself to dealing with high-dimensional data like we have in genomic as well as imaging studies.
What is lasso regression?
The Lasso Regression, a regression method based on Least Absolute Shrinkage and Selection Operator is quite an important technique in regression analysis for selecting the variables and regularization. It gets rid of irrelevant data features that help to prevent overfitting and features with weak influence become more cleanly identifiable because of shrinking the coefficients toward zero.
In this guide, we will understand core concepts of lasso regression as well as how it works to mitigate overfitting.
What is lasso regression?
- Understanding Lasso Regression
- Bias-Variance Tradeoff in Lasso Regression
- How Does Linear Regression works?
- When to use Lasso Regression
- Implementation of Lasso Regression
- Best Practices for Implementing Lasso Regression
- Advantages of Lasso Regression
- Disadvantages of Lasso Regression