Garis besar topik
-
Lasso Regression (Least Absolute Shrinkage and Selection Operator) is a linear regression technique that combines feature selection and regularization to improve model accuracy and prevent overfitting. Lasso works by adding a penalty equal to the absolute sum of the regression coefficients (also known as \(L_1\) regularization) to the loss function. This penalty effectively drives some coefficients to zero, thus retaining only the most important features in the model.
-