Machine Learning with scikit-learn Quick Start Guide

Lasso regression

The equation for lasso regression is as follows:

In the preceding equation, the lasso loss function is equal to the ordinary least squares loss function plus the product of the absolute value of the coefficients of each feature and alpha.

alpha is a parameter that we can optimize to control the amount by which the lasso loss function penalizes the coefficients, in order to prevent overfitting. Once again, if alpha is equal to 0, the lasso loss function is equal to the ordinary least squares loss function, thereby making no difference to the initial overfit model.

Therefore, optimizing this value of alpha provides the optimal ...