logLoss: Log Loss

In JackStat/ModelMetrics: Rapid Calculation of Model Metrics

Description

Calculates the log loss or entropy loss for a binary outcome

Usage

logLoss(...)

## Default S3 method:
logLoss(actual, predicted, distribution = "binomial", ...)

## S3 method for class 'glm'
logLoss(modelObject, ...)

## S3 method for class 'randomForest'
logLoss(modelObject, ...)

## S3 method for class 'glmerMod'
logLoss(modelObject, ...)

## S3 method for class 'gbm'
logLoss(modelObject, ...)

## S3 method for class 'rpart'
logLoss(modelObject, ...)

Arguments

...	additional parameters to be passed the the s3 methods
actual	a binary vector of the labels
predicted	a vector of predicted values
distribution	the distribution of the loss function needed binomial, poisson
modelObject	the model object. Currently supported glm, randomForest, glmerMod, gbm

Examples

data(testDF)
glmModel <- glm(y ~ ., data = testDF, family="binomial")
Preds <- predict(glmModel, type = 'response')

logLoss(testDF$y, Preds)
# using s3 method for glm
logLoss(glmModel)

JackStat/ModelMetrics documentation built on July 6, 2021, 9:48 a.m.