CORElearn (version 1.57.3)

reliabilityPlot: Plots reliability plot of probabilities

Description

Given probability scores probScore and true probabilities trueProb the methods plots one against the other using a selected boxing method which groups scores and probabilities to show calibration of probabilities in given probability bands.

Usage

reliabilityPlot(probScore, trueProb, titleText="", boxing="equipotent", 
                noBins=10, classValue = 1, printWeight=FALSE)

Value

A function returns a graph containing reliability plot on a current graphical device.

Arguments

probScore

A vector of predicted probabilities for a given class classValue.

trueProb

A vector of true probabilities for a given classValue, should be of the same length as probScore.

titleText

The text of the graph title.

boxing

One of "unique", "equidistant" or "equipotent", determines the grouping of probabilities. See details below.

noBins

The value of parameter depends on the parameter boxing and specifies the number of bins. See details below.

classValue

A class value (factor) or an index of the class value (integer) for which reliability plot is made.

printWeight

A boolean specifying if box weights are to be printed.

Author

Marko Robnik-Sikonja

Details

Depending on the specified boxing the probability scores are grouped in one of three possible ways

• "unique" each unique probability score forms its own box.

• "equidistant" forms noBins equally wide boxes.

• "equipotent" forms noBins boxes with equal number of scores in each box.

The parameter trueProb can represent either probabilities (in [0, 1] range, in most cases these will be 0s or 1s), or the true class values from which the method will form 0 and 1 values corresponding to probabilities for class value classValue.

See Also

CORElearn, calibrate.

Examples

# generate data consisting from 3 parts:
#  one part for training, one part for calibration, one part for testing
train <-classDataGen(noInst=200)
cal <-classDataGen(noInst=200)
test <- classDataGen(noInst=200)

# build random forests model with default parameters
modelRF <- CoreModel(class~., train, model="rf")
# prediction of calibration and test set
predCal <- predict(modelRF, cal, rfPredictClass=FALSE)
predTest <- predict(modelRF, test, rfPredictClass=FALSE)
destroyModels(modelRF) # no longer needed, clean up

# show reliability plot of uncalibrated test set
class1<-1
par(mfrow=c(1,2))
reliabilityPlot(predTest$prob[,class1], test$class, 
                titleText="Uncalibrated probabilities", classValue=class1) 

# calibrate for a chosen class1 and method using calibration set
calibration <- calibrate(cal$class, predCal$prob[,class1], class1=1, 
                         method="isoReg", assumeProbabilities=TRUE)
calTestProbs <- applyCalibration(predTest$prob[,class1], calibration)
# display calibrated probabilities
reliabilityPlot(calTestProbs, test$class, 
                titleText="Calibrated probabilities", classValue=class1)