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:

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 ...

Introduction

Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Introduction to scikit-learn and Model Evaluation

Fake News Detection Using Scikit-learn

(Challenge) Logistic Regression and Precision-Recall Curve

Details of Logistic Regression and Feature Extraction

(Challenge) Logistic Regression Model and Coefficients

The Bias-Variance Trade-Off

(Challenge) Cross-Validation and Feature Engineering

Decision Trees and Random Forests

(Challenge) Cross-Validation Grid Search with Random Forest

Gradient Boosting, XGBoost, and SHAP Values

(Challenge) XGBoost and SHAP Explanation for Case Study Data

Predict Frog Toxicity with Python and XGBoost

Test Set Analysis, Financial Insights, and Delivery to the Client

(Challenge) Deriving Financial Insights

Appendix

Exercise: Equal-Interval Chart

Using intervals of predicted probability