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The false discovery rate (FDR) is a statistical metric in multiple hypothesis testing that controls the proportion of false rejections, defined as the expected value of the proportion of rejected null hypotheses that are actually true.
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10/19/2026
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UnverifiedThe false discovery rate (FDR) was proposed by Benjamini and Hochberg in 1995 and is defined as the expected proportion of false discoveries among all findings judged to be significant.76% similarUnverifiedThe false positive rate (FPR) uses all negative samples as its denominator, so when the number of negatives is huge, a seemingly small FPR still produces a large number of false alarms (for example, an FPR of 0.05 over 9,900 negatives corresponds to 495 false alarms).71% similarUnverifiedThe false positive rate is calculated as FPR = FP / (FP + TN) and is one of the core metrics for measuring classifier quality.70% similarUnverifiedIn recommendation scenarios, models typically use Root Mean Square Error (RMSE) to measure the error between predicted ratings and actual ratings.69% similarUnverifiedWhen the prediction error is assumed to follow a Gaussian distribution with zero mean and fixed variance, taking the negative logarithm of the likelihood function and expanding it yields a leading term that is exactly in the form of mean squared error.67% similar
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