T50 {mlquantify} | R Documentation |
Threshold selection method
Description
It quantifies events based on testing scores, applying T50 method proposed by
Forman (2006). It sets the decision threshold of Binary Classifier where
tpr
= 50%.
Usage
T50(test, TprFpr)
Arguments
test |
a numeric |
TprFpr |
a |
Value
A numeric vector containing the class distribution estimated from the test set.
References
Forman, G. (2006, August). Quantifying trends accurately despite classifier error and class imbalance. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 157-166).<doi.org/10.1145/1150402.1150423>.
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"))
T50(test=test.scores[,1], TprFpr=TprFpr)
[Package mlquantify version 0.2.0 Index]