1. Learn
    2. /
  2. Courses
    3. /
  3. Credit Risk Modeling in Python

Exercise

Visually scoring credit models

Now, you want to visualize the performance of the model. In ROC charts, the X and Y axes are two metrics you've already looked at: the false positive rate (fall-out), and the true positive rate (sensitivity).

You can create a ROC chart of it's performance with the following code:

fallout, sensitivity, thresholds = roc_curve(y_test, prob_default)
plt.plot(fallout, sensitivity)

To calculate the AUC score, you use roc_auc_score().

The credit data cr_loan_prep along with the data sets X_test and y_test have all been loaded into the workspace. A trained LogisticRegression() model named clf_logistic has also been loaded into the workspace.

Instructions

100 XP
  • Create a set of predictions for probability of default and store them in preds.
  • Print the accuracy score the model on the X and y test sets.
  • Use roc_curve() on the test data and probabilities of default to create fallout and sensitivity Then, create a ROC curve plot with fallout on the x-axis.
  • Compute the AUC of the model using test data and probabilities of default and store it in auc.