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Lasso and Ridge Regression

Niraj Kumar explains how two regression techniques work:

Lasso Regression is a regularization technique used for feature selection using a Shrinkage method also referred to as the penalized regression method.

Lasso is short for Least Absolute Shrinkage and Selection Operator, which uses both for regularization and model selection.

If a model uses the L1 regularization technique, then known as lasso regression.

Click through for a summary of the two techniques.