T50: Threshold selection method

In mlquantify: Algorithms for Class Distribution Estimation

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 vector containing the score estimated for the positive class from each test set instance.
TprFpr	a data.frame of true positive (tpr) and false positive (fpr) rates estimated on training set, using the function getTPRandFPRbyThreshold().

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)

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