Metrics Glossary

This glossary is a cross-domain lookup index for evaluation metrics used across the wiki. Each row names the metric, gives its field of application, and links to the page that owns the fuller definition, formula, examples, and caveats.

Classification and Probability Calibration

MetricField of applicationMeaning
AccuracyClassificationFraction of examples whose predicted class equals the label.
PrecisionClassificationFraction of predicted positives that are true positives.
RecallClassificationFraction of actual positives found by the model.
F1 scoreClassificationHarmonic mean of precision and recall for a class or averaging scheme.
Balanced accuracyImbalanced classificationAccuracy averaged across classes so a majority class cannot dominate the score.
ROC-AUCBinary ranking and classificationThreshold-free discrimination score based on the receiver operating characteristic curve.
average precisionRare-positive classificationArea-style summary of precision-recall ranking quality, often more informative under extreme imbalance.
Log lossProbabilistic classificationNegative log-likelihood that rewards calibrated probability assigned to the observed label.
Brier scoreProbability calibrationMean squared error between predicted probability and binary outcome.
Expected calibration errorProbability calibrationWeighted gap between predicted confidence and observed accuracy across bins.
SensitivityClinical and safety classificationPositive-class recall, usually emphasizing missed positives.
SpecificityClinical and safety classificationFraction of actual negatives correctly rejected.

Regression

MetricField of applicationMeaning
MAE: mean absolute errorRegressionAverage absolute prediction error in target units.
MSE: mean squared errorRegressionAverage squared prediction error, emphasizing larger misses.
RMSE: root mean squared errorRegressionSquare root of mean squared error, reported in target units.
R-squaredRegressionShare of target variance explained relative to a baseline.

Ranking and Retrieval

MetricField of applicationMeaning
Precision@kRanked retrievalFraction of the visible top results that are relevant.
Recall@kRanked retrievalFraction of known relevant results recovered in the top results.
MAPRanked retrievalMean of average precision scores across queries.
MRRRanked retrievalAverage reciprocal rank of the first relevant result.
NDCGRanked retrievalRank-discounted graded relevance normalized by the ideal ordering.
Rank of first expected sourceRAG retrieval benchmarksPosition of the earliest labelled evidence source in the retrieved list.

Recommendation

MetricField of applicationMeaning
Top-k precisionRecommender rankingFraction of recommended items in a list that match the user’s held-out relevant set.
Top-k recallRecommender rankingFraction of the user’s held-out relevant items recovered by the recommendation list.
Catalog coverageRecommender list healthPortion of the item catalog or user space reached by recommendations.
Intra-list diversityRecommender list healthDegree to which items in the same recommendation list differ from one another.
NoveltyRecommender list healthDegree to which recommended items are not already obvious or popular.
SerendipityRecommender list healthDegree to which recommendations are both unexpected and useful.

Time-Series Forecasting

MetricField of applicationMeaning
MAEPoint forecastingAverage absolute forecast error in the target unit.
RMSEPoint forecastingSquare root of average squared forecast error, emphasizing large misses.
MAPEPoint forecastingAverage absolute percentage error, unstable when actuals are zero or near zero.
WAPEDemand and portfolio forecastingTotal absolute error divided by total actual volume.
MASECross-series forecastingAbsolute error scaled by a naive or seasonal-naive baseline.
Forecast biasPoint forecastingSigned average error showing systematic overforecasting or underforecasting.
pinball lossProbabilistic forecastingAsymmetric loss for evaluating a forecasted quantile.
Empirical interval coveragePrediction intervalsFraction of realized values falling inside predicted intervals.
Interval widthPrediction intervalsSize of the predicted interval, reported alongside coverage.
SharpnessProbabilistic forecastingConcentration of a predictive distribution when calibration is acceptable.
Forecast calibrationProbabilistic forecastingAgreement between predicted quantiles or intervals and observed frequencies.

NLP and Text Generation

MetricField of applicationMeaning
Macro-F1NLP classificationClass-level F1 averaged equally across labels.
Micro-F1NLP classificationF1 computed from aggregated counts across labels.
Span F1Sequence labelling and extractionF1 over matched spans rather than only token labels.
Field exact matchInformation extractionWhether an extracted field value exactly matches the reference value.
Field exact accuracyInformation extractionFraction of schema fields whose extracted values exactly match references.
Character error rateOCR and transcriptionEdit distance normalized by reference character count.
Word error rateOCR and speech-style transcriptionWord-level edit distance normalized by reference word count.
PerplexityLanguage modellingExponentiated average negative log-likelihood per predicted token.
BLEUMachine translation and generationReference-overlap score based on modified n-gram precision.

Computer Vision and Video

MetricField of applicationMeaning
Intersection over unionDetection and segmentationOverlap divided by union between predicted and reference regions.
Dice coefficientSegmentationTwice the overlap divided by combined predicted and reference region sizes.
Average precisionObject detectionPrecision-recall summary after confidence sorting and overlap-based matching.
mAPObject detectionMean detection average precision across classes and often overlap thresholds.
Pixel accuracySemantic segmentationFraction of pixels assigned the correct class.
Mean IoUSemantic segmentationAverage class-wise region overlap score.
Panoptic QualityPanoptic segmentationSegment overlap penalized by unmatched predicted and reference segments.
Boundary F-scoreSegmentationBoundary precision and recall with a spatial tolerance.
Hausdorff distanceMedical segmentationWorst nearest-surface error between predicted and reference boundaries.
ASSDMedical segmentationAverage symmetric surface distance between predicted and reference surfaces.
Surface DiceMedical segmentationFraction of surface points within an acceptable distance tolerance.
PCKPose estimationFraction of visible keypoints within a normalized distance threshold.
Temporal IoUTemporal localizationOverlap divided by union for predicted and reference time segments.
Temporal mAPTemporal action detectionMean average precision for time segments across temporal overlap thresholds.

Clustering and Representation

MetricField of applicationMeaning
Silhouette scoreClusteringGeometry-only score comparing within-cluster distance with nearest other-cluster distance.
Adjusted Rand indexClustering with labelsChance-adjusted agreement between a clustering and reference labels.

Generative AI, RAG, and Agents

MetricField of applicationMeaning
Context recallRAG retrievalFraction of expected evidence recovered into the model context.
Citation precisionRAG answersFraction of cited sources that correspond to expected or supporting evidence.
Citation coverageRAG answersDegree to which answer claims or required facts have citations.
Answer supportRAG answersDegree to which generated claims are backed by retrieved evidence.
Claim support rateRAG answersFraction of checked answer claims judged supported by evidence.
Abstention qualityRAG and generative systemsWhether the system refuses or answers appropriately when evidence is missing.
Task successGenerative task systemsWhether the final output satisfies the task-specific success criteria.
Pass predicateAgent evaluationBoolean conjunction of outcome correctness, required actions, forbidden-action absence, and budget compliance.
Budget complianceAgent evaluationWhether a trace stays within resource, latency, or call limits.
Source coverageRAG and evaluation datasetsDegree to which required source documents or evidence categories are exercised.

Experiment and Agreement Statistics

MetricField of applicationMeaning
p-valueHypothesis testingProbability of a statistic at least as extreme under a specified null model.
Confidence intervalStatistical estimationRepeated-sampling interval procedure with nominal long-run parameter coverage.
Statistical powerExperiment planningProbability of detecting a specified effect under the planned test design.
Bootstrap intervalEvaluation uncertaintyInterval estimated by resampling examples and recomputing a statistic.
Evaluation coverageEvaluation datasetsFraction of required slices, cases, sources, or paths represented in the evaluation.
Raw agreementHuman evaluationFraction of reviewer labels that match before chance adjustment.
Cohen’s kappaHuman evaluationReviewer agreement adjusted for expected chance agreement.

How to use this page

Use this glossary when a metric name appears before its full explanation. For study, jump from the metric to the owning subject area: classification and regression metrics usually live in classical machine learning, forecast metrics in time-series forecasting, ranked-list metrics in search or recommendation systems, and judge or trace metrics in generative AI and experimentation.