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Krahasoni metodat

Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.

Saktësi×Saktësi×Kujtesë (ndjeshmëri)×
FushaVlerësimi i modeleveVlerësimi i modeleveVlerësimi i modeleve
FamiljaMCDMMCDMMCDM
Viti i origjinës20th century20th century20th century
KrijuesiHistorical statistical foundationsHistorical statistical foundationsHistorical statistical foundations
LlojiEvaluation metricEvaluation metricEvaluation metric
Burimi themeluesFawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗
Emërtime të tjeraPositive Predictive Value, PPVOverall Accuracy, Correct Classification RateSensitivity, True Positive Rate, TPR
Të lidhura555
PërmbledhjaPrecision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.
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  2. 2 Burimet
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ScholarGateKrahasoni metodat: Precision · Accuracy · Recall (Sensitivity). Marrë më 2026-06-18 nga https://scholargate.app/sq/compare