Using sparsity to regularize models

The least absolute shrinkage and selection operator (LASSO) method is very similar to ridge regression and least angle regression (LARS). It's similar to ridge regression in the sense that we penalize our regression by an amount, and it's similar to LARS in that it can be used as a parameter selection, typically leading to a sparse vector of coefficients. Both LASSO and LARS get rid of a lot of the features of the dataset, which is something you might or might not want to do depending on the dataset and how you apply it. (Ridge regression, on the other hand, preserves all features, which allows you to model polynomial functions or complex functions with correlated features.)

Getting ready

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