Lasso Regression
Lasso Regression is a linear regression technique that performs both variable selection and regularization to improve the prediction accuracy and interpretability of the model. It adds a penalty term to the cost function of the linear regression model, which shrinks the coefficients of the less important features to zero, effectively removing them from the model. This helps to prevent overfitting and improve the generalization performance of the model. Lasso Regression is particularly useful when dealing with high-dimensional datasets with many features, where it can automatically select the most relevant features and discard the irrelevant ones. It is also commonly used in feature engineering and feature selection tasks, as it can provide insights into the importance of different features in the dataset.
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