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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

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    $\begingroup$ What is the reason you want to give more weights to those variables? $\endgroup$ – Metariat Sep 2 '15 at 8:11
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In general let a forest algorithm up-weight/down-weight use of variables. I notice you have set mtry=1. Then, only one random variable is picked in every split. If you expect some variables would be more useful, increase mtry to e.g. a 1/3(default) of total variable number to allow the RF model to use some variables more than others. If a variable is very useful, it will achieve better loss function scores and get picked more often. If mtry is too high tree-decorrelation may suffer, and probably an optimal mtry is less than 2/3 of total variable number.

You can also utilize variable importance to check if some variables are 'trash' and rerun the model without them. Be-aware of mild over-fitting when selecting variables like this.

It is possible to train an ensemble where different sub-models are weighted accordingly to some learned function of feature inputs. But that would probably be too excessive in your case.

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