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InformationValue (version 1.2.3)

precision: precision

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.

Value

The precision or the positive predictive value.

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.

Examples

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

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