PACC {mlquantify}

Probabilistic Adjusted Classify and Count

Description

It quantifies events based on testing scores, applying the Probabilistic Adjusted Classify and Count (PACC) method. This method is also called Scaled Probability Average (SPA).

Usage

PACC(test, TprFpr, thr=0.5)

Arguments

test

a numeric vector containing the score estimated for the positive class from each test set instance. (NOTE: It requires calibrated scores. See calibrate from CORElearn).

TprFpr

a data.frame of true positive (tpr) and false positive (fpr) rates estimated on training set, using the function getTPRandFPRbyThreshold().

thr

threshold value according to the tpr and fpr were learned. Default is 0.5.

Value

A numeric vector containing the class distribution estimated from the test set.

References

Bella, A., Ferri, C., Hernández-Orallo, J., & Ramírez-Quintana, M. J. (2010). Quantification via probability estimators. In IEEE International Conference on Data Mining (pp. 737–742). Sidney.<doi.org/10.1109/ICDM.2010.75>.

Examples

library(randomForest)
library(caret)
cv <- createFolds(aeAegypti$class, 3)
tr <- aeAegypti[cv$Fold1,]
validation <- aeAegypti[cv$Fold2,]
ts <- aeAegypti[cv$Fold3,]

# -- Getting a sample from ts with 80 positive and 20 negative instances --
ts_sample <- rbind(ts[sample(which(ts$class==1),80),],
                   ts[sample(which(ts$class==2),20),])
scorer <- randomForest(class~., data=tr, ntree=500)
scores <- cbind(predict(scorer, validation, type = c("prob")), validation$class)
TprFpr <- getTPRandFPRbyThreshold(scores)
test.scores <- predict(scorer, ts_sample, type = c("prob"))[,1]

# -- PACC requires calibrated scores. Be aware of doing this before using PACC --
# -- You can make it using calibrate function from the CORElearn package --
# if(requireNamespace("CORElearn")){
#    cal_tr <- CORElearn::calibrate(as.factor(scores[,3]), scores[,1], class1=1,
#    method="isoReg",assumeProbabilities=TRUE)
#    test.scores <- CORElearn::applyCalibration(test.scores, cal_tr)
#}
PACC(test = test.scores, TprFpr = TprFpr)