SORD {mlquantify} | R Documentation |
Sample ORD Dissimilarity
Description
It quantifies events based on testing scores applying the framework DyS with the Sample ORD Dissimilarity (SORD) proposed by Maletzke et al. (2019).
Usage
SORD(p.score, n.score, test)
Arguments
p.score |
a numeric |
n.score |
a numeric |
test |
a numeric |
Value
A numeric vector containing the class distribution estimated from the test set.
References
Maletzke, A., Reis, D., Cherman, E., & Batista, G. (2019). DyS: a Framework for Mixture Models in Quantification. in Proceedings of the The Thirty-Third AAAI Conference on Artificial Intelligence, ser. AAAI’19, 2019.<doi.org/10.1609/aaai.v33i01.33014552>.
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)
test.scores <- predict(scorer, ts_sample, type = c("prob"))
SORD(p.score = scores[scores[,3]==1,1], n.score = scores[scores[,3]==2,1],
test = test.scores[,1])