CC: Classify and Count

In mlquantify: Algorithms for Class Distribution Estimation

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 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

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.