Compute and plot a precision–recall curve

Q: Compute and plot a precision–recall curve

This question evaluates understanding of binary classification evaluation metrics—precision, recall, precision–recall curves, and related summaries like Average Precision/AUPRC—within the Machine Learning domain and is relevant for Data Scientist roles.

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Question

Precision–Recall (PR) curve coding / evaluation

You are given a binary classifier’s outputs on a dataset:

y_true : array of true labels in $\{0,1\}$
y_score : array of predicted scores/probabilities (higher means more likely positive)

Tasks

Define precision and recall .
Describe how to compute the precision–recall curve by sweeping a decision threshold over y_score .
Implement (in pseudocode or Python) a function that returns PR curve points:
- Output arrays: thresholds , precision , recall
Mention at least two edge cases/pitfalls (e.g., ties in scores, no predicted positives at a threshold, extreme class imbalance).

Optional: Explain how to compute Average Precision / AUPRC and what the baseline means.

Compute and plot a precision–recall curve

Precision–Recall (PR) curve coding / evaluation

Tasks

Solution

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Compute and plot a precision–recall curve

Overview

Precision–Recall (PR) curve coding / evaluation

Tasks

Solution

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