mlquantify (version 0.2.0)

ACC: Adjusted Classify and Count

Description

It quantifies events based on testing scores using the Adjusted Classify and Count (ACC) method. ACC is an extension of CC, applying a correction rate based on the true and false positive rates (tpr and fpr).

Usage

ACC(test, TprFpr, thr=0.5)

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().

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

Forman, G. (2006, August). Quantifying trends accurately despite classifier error and class imbalance. In ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 157-166).<doi.org/10.1145/1150402.1150423>.

Examples

# NOT RUN {
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"))
ACC(test = test.scores[,1], TprFpr = TprFpr)
# }