Explore lasso regression penalty values

In the previous exercise, you completed all the code to scale the target and predictor variables. You will use the train data and lasso_recipe to build a workflow to train a lasso regression model and explore the effects of different penalty values. As you adjust the penalty and retrain the model, pay attention to the number of non-zero variables that are left in the model. You will be observing how lasso regression performs feature selection.

The tidyverse and tidymodels packages have been loaded for you.

Train a lasso regression workflow with a penalty of 0.001 and display the model coefficients that are greater than zero.
Re-train a lasso regression workflow with a penalty of 0.01 and display the model coefficients that are greater than zero.
Re-train a lasso regression workflow with a penalty of 0.1 and display the model coefficients that are greater than zero.

Exercise

Explore lasso regression penalty values

Instructions