## False Discovery Rate

The FDR relates the number of false positives to the total number of positively predicted elements. This estimates a likelihood of an element being negative, if it is predicted positive. This measure is based on the pairwise approach to calculate TP,TN,FP and FN.
\[ FDR = \frac{FP}{FP+TP} \]
In the binary classification background we have two classes that we want to distinguish:

**positive** and

**negative**.
In this scenario there are four possible outcomes:

**TP (True Positive):** The object belongs to class **positive** and we classified it as **positive**,
**FP (False Positive ):** The object belongs to class **negative** and we classified it as **positive**,
**TN (True Negative):** The object belongs to class **negative** and we classified it as **negative**,
**FN (False Negative):** The object belongs to class **positive** but we classified it as **negative**

| | Reality |

| | Positive | Negative |

Prediction | Positive | TP | FP |

Negative | FN | TN |