Precision-recall curve
A precision-recall curve is a plot of a classifier’s precision against recall as its decision threshold varies.
Grokking Statistics
A precision-recall curve plots a classifier's precision against its recall across decision thresholds.
- How it is used
- Evaluates classifier performance across thresholds, especially when imbalanced classes make accuracy misleading.
- How deep it goes
- Explains threshold tradeoffs, confusion matrices, dataset size, and AUC, but does not derive the curve mathematically.
- Examples
- Uses toddler meltdown prediction, nap duration, a 100-sample dataset, Figure 8.9, and scikit-learn curve code.
- What you can do after
- Evaluate classifier thresholds with a precision-recall curve and summarize performance using AUC.
Read how Grokking Statistics explains precision-recall curve →
Machine Learning Algorithms in Depth
A precision-recall curve plots precision against recall across classification thresholds.
- How it is used
- Evaluates binary-classifier performance under changing classification thresholds, especially in information retrieval.
- How deep it goes
- Defines both axes, relates them to confusion-matrix counts, and adds mean precision and F1 summaries.
- Examples
- Uses a precision–recall plot and contrasts it visually with a receiver operating characteristic plot.
- What you can do after
- Evaluate classifier performance across thresholds and summarize it with mean precision or F1.
Read how Machine Learning Algorithms in Depth explains precision-recall curve →
Related concepts
- area under the curvemeasured by · The area under a curve is the integral of its values over a specified domain and, in classification evaluation, commonly summarizes performance across decision thresholds.
- recalluses · Recall is the fraction of relevant, positive, or target outcomes that a system successfully identifies, retrieves, or captures.
- precisionuses · Precision is the proportion of items identified as positive, selected, or retrieved that satisfy the relevant correctness or relevance criterion.
- receiver operating characteristicalternative to · A receiver operating characteristic curve plots a binary classifier’s true-positive rate against its false-positive rate as the decision threshold varies.
- class imbalanceused by · Class imbalance is a condition in a classification dataset where classes have substantially unequal numbers of examples.
- fraud detectionmeasures · Fraud detection is the process of identifying transactions, activities, or other events suspected of involving intentional deception or unauthorized gain.
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