so, I've found the misclassified instances in my Random forest model have lower values in some predictors, how can I adjust the model so that the threshold is more sensitive to these predictors?
fit1 <- cforest((b == 'three')~ affect+ certain+ negemo+ future+swear+sad +negate+ppron+sexual+death + filler+leisure + conj+ funct + i +future + past + bio + body+cause + cogmech + death + discrep + future +incl + motion + quant + sad + tentat + excl+insight +percept +posemo +ppron +quant + relativ + space + article , data = trainset1, controls=cforest_unbiased(ntree=1000, mtry= 1)) table1 <- table(predict(fit1, OOB=TRUE, type = 'response') > 0.5, trainset1$b == 'three') result FALSE TRUE FALSE 213 200 TRUE 821 1121
As the result has shown, 821 of other classes are misclassified as "three", suppose I want to adjust the model so that it is more sensitive to these variables: negemo, posemo, swear. What should I do? Thank you