precision: precision

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

Description

Calculate the precision or positive predictive value for a given set of actuals and predicted probability scores.

Usage

precision(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

For a given given binary response actuals and predicted probability scores, precision is defined as the proportion of observations with the event out of the total positive predictions.

Value

The precision or the positive predictive value.

Author(s)

Selva Prabhakaran [e-mail]

Examples

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

InformationValue documentation built on May 1, 2019, 9:12 p.m.