False Discovery Rate

Description

For classification tasks, the terms true positives, true negatives, false positives, and false negatives compare the results of the classifier under test with trusted external judgments. The terms positive and negative refer to the classifier’s prediction (sometimes known as the expectation), and the terms true and false refer to whether that prediction corresponds to the external judgment. False discovery rate (FDR) control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons and it is the complement of the positive predictive value. It measures the proportion of actual positives which are incorrectly identified.

Related formulas

Variables

FDRFalse Discovery Rate (dimensionless)
FPNumber of False Positive (incorrectly identified) (dimensionless)
TPNumber of True Positives (correctly identified) (dimensionless)