Final answer:
Lasso regression is a form of linear regression that includes an L1 penalty to encourage coefficient sparsity, thus performing feature selection and preventing overfitting. It is particularly useful in datasets with a high number of features and emphasizes predictive features by shrinking less relevant ones to zero.
Step-by-step explanation:
Lasso regression is a statistical method that enhances the prediction accuracy and interpretability of the regression model you are using in data analysis. It does this by adding a penalty equivalent to the absolute value of the magnitude of coefficients to the usual least-squares criterion of linear regression. This penalty term is the L1 regularization, which encourages sparsity in the coefficients. In simpler terms, Lasso regression can shrink some coefficients to zero, effectively performing feature selection and allowing for a more parsimonious model.
The use of Lasso regression is particularly useful when dealing with datasets with a large number of features. By shrinking the less important feature's coefficients to zero, it automatically selects more relevant features, helping to prevent overfitting. The resulting model is simpler and may highlight the features that have the most predictive power for the dependent variable.
To better understand Lasso regression, consider the relationship between explanatory variables (predictors) and a response variable. In a simple linear regression model, we look to minimize the sum of squared errors (SSE), which measures the difference between the predicted and actual y-values. However, Lasso regression takes a step further by not only minimizing SSE but also penalizing the size of the regression coefficients to foster a model with fewer variables.