CC: Classify and Count

Description Usage Arguments Value References Examples

View source: R/CC_method.r

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

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CC(test, thr=0.5)

Arguments

test

a numeric vector containing the score estimated for the positive class from each test set instance.

thr

a numeric value indicating the decision threshold. A value between 0 and 1 (default = 0.5)

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

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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])

mlquantify documentation built on April 13, 2021, 5:08 p.m.