PCC: Probabilistic Classify and Count

In mlquantify: Algorithms for Class Distribution Estimation

Description

It quantifies events based on testing scores, applying the Probabilistic Classify and Count (PCC) method.

Usage

PCC(test)

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).

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)
test.scores <- predict(scorer, ts_sample, type = c("prob"))[,1]

# -- PCC requires calibrated scores. Be aware of doing this before using PCC --
# -- 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)
# }
PCC(test=test.scores)

mlquantify documentation built on Jan. 20, 2022, 5:07 p.m.