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  2. False positive rate - Wikipedia

    en.wikipedia.org/wiki/False_positive_rate

    The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification). The false positive rate (or "false alarm rate") usually refers to the expectancy of the false positive ratio.

  3. False positives and false negatives - Wikipedia

    en.wikipedia.org/wiki/False_positives_and_false...

    The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.

  4. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    The probability of type I errors is called the "false reject rate" (FRR) or false non-match rate (FNMR), while the probability of type II errors is called the "false accept rate" (FAR) or false match rate (FMR). If the system is designed to rarely match suspects then the probability of type II errors can be called the "false alarm rate". On the ...

  5. Sensitivity and specificity - Wikipedia

    en.wikipedia.org/wiki/Sensitivity_and_specificity

    An estimate of d′ can be also found from measurements of the hit rate and false-alarm rate. It is calculated as: d′ = Z(hit rate) − Z(false alarm rate), [15] where function Z(p), p ∈ [0, 1], is the inverse of the cumulative Gaussian distribution. d′ is a dimensionless statistic. A higher d′ indicates that the signal can be more ...

  6. Template:Diagnostic testing diagram - Wikipedia

    en.wikipedia.org/wiki/Template:Diagnostic...

    False positive (FP), false alarm, overestimation: True negative (TN), correct rejection [e] False positive rate (FPR), probability of false alarm, fall-out

  7. Fairness (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Fairness_(machine_learning)

    For example, we can add to the objective of the algorithm the condition that the false positive rate is the same for individuals in the protected group and the ones outside the protected group. The main measures used in this approach are false positive rate, false negative rate, and overall misclassification rate.

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  9. Template:DiagnosticTesting Diagram - Wikipedia

    en.wikipedia.org/wiki/Template:DiagnosticTesting...

    True positive rate (TPR), Recall, Sensitivity (SEN), probability of detection, Power = ⁠ Σ True positive / Σ Condition positiveFalse positive rate (FPR), Fall-out, probability of false alarm = ⁠ Σ False positive / Σ Condition negative ⁠ Positive likelihood ratio (LR+) = ⁠ TPR / FPR ⁠ Diagnostic odds ratio (DOR) = ⁠ LR+ / LR ...