Lasso

TL;DR

Selects a subset of predictors from a larger set to simplify models.
Applies coefficient shrinkage to help avoid overfitting.
Improves model interpretability and can increase predictive accuracy.

Definition

Lasso is a type of regression analysis that uses shrinkage and variable selection to improve the accuracy and interpretability of the model.

Explanation

Lasso combines coefficient shrinkage with variable selection so that, from a large set of potential predictors, only the most important ones remain in the final model. This makes the model easier to interpret and helps prevent overfitting, which can improve predictive accuracy. It is commonly used by data analysts and researchers working with many potential predictors.

Examples

Financial analysis

A company predicting a stock price based on factors such as revenue, earnings per share, and debt-to-equity ratio can use lasso to identify which predictors are most strongly associated with the stock price and include only those in the final model. This helps improve prediction accuracy and interpretability.

Medical research

A researcher predicting the likelihood of a patient developing a disease from medical history, lifestyle factors, and genetic data can use lasso to determine which predictors are most strongly associated with disease likelihood and include only those in the final model. This can improve prediction accuracy and make the model more interpretable.

Use cases

Identifying the most important predictors in a large dataset.
Avoiding overfitting in regression models.
Producing more interpretable predictive models.

Shrinkage
Variable selection
Overfitting
Interpretability