PR Summary

This pull request refines the documentation and commentary of the function that visualizes cumulative prediction probabilities for classification models. The changes focus on simplifying the explanation by removing redundant references to separate training and testing datasets. Instead, the documentation now uniformly describes the evaluation process using a single dataset, where predicted probabilities are computed, added as a column, and then sorted to create cumulative distributions for both positive and negative classes.

The updated description also refines the explanation of the metric’s purpose, including its strengths, limitations, and potential calibration issues. The text now emphasizes that the visualization is used to identify abnormalities or imbalances in the model predictions, with a more streamlined narrative avoiding unnecessary dataset separation.

Test Suggestions

• Run the function with a known dataset and verify that the cumulative probability plot correctly shows the cumulative distributions for positive and negative classes.
• Check that the new column for predicted probabilities is added to the dataset as expected.
• Ensure that the plot layout and labels match the updated documentation.