Mission accomplished

To approach this mission, you have reduced overfitting using primarily the toolset of feature selection. The non-profit is pleased with a profit lift of roughly 30%, costing a total of , which is ,000 less than it would cost to send everyone in the test dataset the mailer. However, they still want assurance that they can safely employ this model without worries that they'll experience losses.

In this chapter, we've examined how overfitting can cause the profitability curves not to align. Misalignment is critical because it could mean that choosing a threshold based on training data would not be reliable on out-of-sample data. So, you use compare_df_plots to compare profitability between the test and train sets as you've done before, but this time for the chosen model (rf_5_e-llarsic):

profits_test = reg_mdls['rf_5_e-llarsic']['profits_test']
profits_train = reg_mdls['rf_5_e-llarsic']['profits_train']...