Helpful examples of using Lasso Regularization machine learning algorithms in scikit-learn.
The Lasso (Least Absolute Shrinkage and Selection Operator) algorithm is a regularized regression technique used to enhance model accuracy and interpretability.
It introduces an L1 penalty to the loss function, which involves the absolute values of the regression coefficients.
This penalty encourages the model to shrink some coefficients to exactly zero, effectively performing variable selection by excluding less important predictors.
Lasso is particularly useful when dealing with high-dimensional data, where it helps prevent overfitting by creating simpler, more interpretable models. However, it can struggle with highly correlated variables, as it tends to select only one variable from a group of highly correlated predictors.
Despite this limitation, Lasso is a powerful tool for producing sparse models that highlight the most significant predictors.