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).
a numeric value indicating the decision threshold. A value between 0 and 1 (default =
A numeric vector containing the class distribution estimated from the test set.
Forman, G. (2005). Counting positives accurately despite inaccurate classification. In European Conference on Machine Learning. Springer, Berlin, Heidelberg.<doi.org/10.1007/11564096_55>.
1 2 3 4 5 6 7 8 9 10 11 12
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])
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.