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Sensitivity ( true positive rate)

For classification tasks, the terms true positives, true negatives, false positives, and false negatives compare the results of the classifier under test ... more

Specificity ( true negative rate)

For classification tasks, the terms true positives, true negatives, false positives, and false negatives compare the results of the classifier under test ... more

False Omission Rate

For classification tasks, the terms true positives, true negatives, false positives, and false negatives compare the results of the classifier under test ... more

False Discovery Rate

For classification tasks, the terms true positives, true negatives, false positives, and false negatives compare the results of the classifier under test ... more

Negative Predictive Value

For classification tasks, the terms true positives, true negatives, false positives, and false negatives compare the results of the classifier under test ... more

Positive Predictive Value (precision)

For classification tasks, the terms true positives, true negatives, false positives, and false negatives compare the results of the classifier under test ... more

Accuracy

The accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity’s actual (true) value. Accuracy is ... more

Fβ-score (in terms of Type I and type II errors)

In statistical analysis of binary classification, the F1 score (also F-score or F-measure) is a measure of a test’s accuracy. ( a type I error is ... more

Matthews correlation coefficient

The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications. It takes into account ... more

Sharpe ratio

In finance, the Sharpe ratio (also known as the Sharpe index, the Sharpe measure, and the reward-to-variability ratio) is a way to examine the performance ... more

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