Compute precision–recall curve on imbalanced data

Q: Compute precision–recall curve on imbalanced data

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Question

You receive a CSV with columns: actual_label ∈ {0,1} and predicted_prob ∈ [0,1]; the positive class rate is ≈5%. a) Which evaluation metrics would you prioritize and why (PR curve/AUPRC, calibration, cost‑sensitive metrics; pitfalls of accuracy/ROC in heavy imbalance)? b) Write a Python function that returns thresholds, precision, recall, and F1 across all unique predicted_prob values; handle ties, empty denominators, and enforce monotonic precision if needed. c) Compute AUPRC efficiently and discuss the effect of score calibration on the PR curve. d) Describe how you would pick an operating threshold given asymmetric costs and volume constraints.

Compute precision–recall curve on imbalanced data

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