Verified65% confidenceFactTime unknown
The formula for Accuracy is (TP+TN)/(P+N), i.e., the proportion of correctly predicted samples out of the total number of samples.
3
Sources
65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
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UnverifiedPrecision refers to the proportion of samples the model identifies as positive that are truly positive; recall refers to the proportion of all true positives that the model successfully identifies; the two typically have an inverse relationship.74% similarUnverifiedOpportunity-normalized accuracy is computed as (model accuracy − random-guess accuracy) / (1 − random-guess accuracy).70% similarUnverifiedMacro-average accuracy first computes the accuracy of each class separately and then takes the arithmetic mean, making it more sensitive to the performance of minority classes; overall accuracy is the proportion of all samples predicted correctly and is easily affected by the class sample size distribution.69% similarUnverifiedResearchers have proposed having models explicitly output probabilities, or estimating reliability by sampling multiple times and measuring answer consistency (self-consistency).68% similarUnverifiedThe accuracy data in the test has an error margin of approximately ±8–10 percentage points at n=100 sample size (based on a 95% confidence-level binomial confidence interval).66% similar
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