Lasso Path is a method which uses a linear model with a L1 regularization and computes the LASSO path (ie. for all values of the regularization parameter).

This computation allows to select a given number of non-zero coefficients, ie. to select a given number of features. After training, you will be able to visualize the LASSO path and select a new number of features.

This is performed using LARS regression. It requires a number of passes on the data equal to the number of features. If this number is large, computation may be slow. It usually requires a large number of passes on the data, and as such may be unsuited for large datasets.