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.)
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Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.
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Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.
Read more about Trent Hauck