fscore: fscore

In selva86/InformationValue: Performance Analysis and Companion Functions for Binary Classification Models

Description

Computes the F1 Score for a given pair of actuals and predicted scores

Usage

fscore(actuals, predictedScores, threshold = 0.5)

Arguments

actuals	The actual binary flags for the response variable. It can take a numeric vector containing values of either 1 or 0, where 1 represents the 'Good' or 'Events' while 0 represents 'Bad' or 'Non-Events'.
predictedScores	The prediction probability scores for each observation. If your classification model gives the 1/0 predcitions, convert it to a numeric vector of 1's and 0's.
threshold	If predicted value is above the threshold, it will be considered as an event (1), else it will be a non-event (0). Defaults to 0.5.

Details

F1 Score is computed as a weighted average of precision and recall. F1 score reaches its best value at 1 and worst at 0. F1 Score = (2 * recall * precision) / (recall + precision)

Value

The F1 Score

Author(s)

Selva Prabhakaran [e-mail]

Examples

data('ActualsAndScores')
fscore(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores)

selva86/InformationValue documentation built on May 29, 2019, 5:55 p.m.