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Exercise: Equal-Interval Chart

Explore how to create equal-interval bins for predicted probabilities to analyze default rates in a test set. Understand how to calculate and visualize default rates with error bars, interpreting sample size effects across bins.

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Using intervals of predicted probability

In this exercise, you'll make a plot similar to the "Default rate according to model prediction decile" plot from the last lesson. However, instead of splitting the test set into equal-population deciles of predicted probability, you'll use equal intervals of predicted probability. Specifying the intervals could be helpful if a business partner wants to think about potential model-based strategies using certain score ranges. You can use pandas cut to create equal-interval binnings, or custom binnings using an array of bin edges, similar to how you used qcut to create quantile labels.

Perform the following steps to complete the exercise:

  1. Create the series of equal-interval labels, for 5 bins, using the following code:

    equal_intervals, equal_interval_bin_edges = \
 pd.cut(x=test_set_pred_proba,\
 bins=5,\
 retbins=True)

    Notice that this is similar to the call to qcut, except here with cut we can say how many equal-interval bins we want by supplying an integer to the bins ...