An embedded method selects features from the parameters or importance values of a model trained as part of the selection process. It integrates feature selection with model fitting, unlike filter methods, which rank features independently, or wrapper methods, which search subsets using model quality. section 11.3.3
Lasso regression
Lasso regression eliminates features as its L1 regularization term grows by reducing their coefficients to zero, making the model parameters drive feature selection. section 11.3.3
Figure 11.9 Lasso regression eliminates features one by one by reducing their coefficients to zero as L1 regularization term grows.
Use the figure to see how increasing L1 regularization drives coefficients to zero and removes features. section 11.3.3
