Exercise

# Comparing with ROCs

You should use ROC charts and AUC scores to compare the two models. Sometimes, visuals can really help you and potential business users understand the differences between the various models under consideration.

With the graph in mind, you will be more equipped to make a decision. The lift is how far the curve is from the random prediction. The AUC is the area between the curve and the random prediction. The model with more lift, and a higher AUC, is the one that's better at making predictions accurately.

The trained models `clf_logistic`

and `clf_gbt`

have been loaded into the workspace. The predictions for the probability of default `clf_logistic_preds`

and `clf_gbt_preds`

have been loaded as well.

Instructions 1/2

**undefined XP**

- Calculate the
`fallout`

,`sensitivity`

, and`thresholds`

for the logistic regression and gradient boosted tree. - Plot the ROC chart for the
`lr`

then`gbt`

using the`fallout`

on the x-axis and`sensitivity`

on the y-axis for each model.