Examples

Use these patterns to compare models and datasets.

Clear separation (breast cancer, scikit-learn)

• Load sklearn.datasets.load_breast_cancer.
• Train a logistic regression or gradient boosting model.
• Plot the operating profile on the test split to inspect separability.
• Interpretation: distributions are well separated; TPR stays high while FPR stays low across much of the threshold range.

Ambiguous scores (overlapping normals)

• Simulate scores from two overlapping normal distributions with similar means/variance.
• Expect intertwined histograms and TPR/FPR curves that cross more frequently.
• Interpretation: thresholds are fragile; small shifts move a lot of examples between classes.

Bumpy distributions (mixed pockets)

• Build a model that produces multi-modal scores (e.g., mixture components or segment-specific calibrations).
• Look for “bumps” in the histogram and corresponding inflections in TPR/FPR.
• Interpretation: localized score clusters may indicate subpopulations; thresholding there can create sharp metric changes.

Swap in your own datasets; the plotting API stays the same.