(1.8 hours to learn)


The Lasso is a form of regularized linear regression. Unlike ridge regression, it puts an L1 penalty on the weights, which encourages sparsity, i.e. it encourages most of the weights to be exactly zero. The general trick of using L1 norms to encourage sparsity is widely used in machine learning.


This concept has the prerequisites:

Core resources (read/watch one of the following)


The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman


Supplemental resources (the following are optional, but you may find them useful)


See also