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

## Description

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 detecting an effect that is not present, while a type II error is failing to detect an effect that is present. The terms “type I error” and “type II error” are often used interchangeably with the general notion of false positives and false negatives ). Fβ “measures the effectiveness of retrieval with respect to a user who attaches β times as much importance to recall as precision”.

Related formulas## Variables

F_β | Fβ-score (dimensionless) |

β | Shape parameter (dimensionless) |

TP | Number of True Positives (correctly identified) (dimensionless) |

FN | Number of False Negatives ( incorrectly rejected) (dimensionless) |

FP | Number of False Positive (incorrectly identified) (dimensionless) |