CC {mlquantify} | R Documentation |
Classify and Count
Description
It quantifies events based on testing scores, applying the Classify and Count (CC). CC is the simplest quantification method that derives from classification (Forman, 2005).
Usage
CC(test, thr=0.5)
Arguments
test |
a numeric |
thr |
a numeric value indicating the decision threshold. A value between 0 and 1 (default = |
Value
A numeric vector containing the class distribution estimated from the test set.
References
Forman, G. (2005). Counting positives accurately despite inaccurate classification. In European Conference on Machine Learning. Springer, Berlin, Heidelberg.<doi.org/10.1007/11564096_55>.
Examples
library(randomForest)
library(caret)
cv <- createFolds(aeAegypti$class, 2)
tr <- aeAegypti[cv$Fold1,]
ts <- aeAegypti[cv$Fold2,]
# -- Getting a sample from ts with 80 positive and 20 negative instances --
ts_sample <- rbind(ts[sample(which(ts$class==1),80),],
ts[sample(which(ts$class==2),20),])
scorer <- randomForest(class~., data=tr, ntree=500)
test.scores <- predict(scorer, ts_sample, type = c("prob"))
CC(test = test.scores[,1])
[Package mlquantify version 0.2.0 Index]