I'm working on a classification problem with continous and categorical predictors with Random Forests (RF). I'm particularly interested on RF as we avoid the specification of the functional form.
However when it comes to the partial dependence for categorical variables, I'm not sure how to interpret it. For instance, the partial dependence (with the command
partialPlot in the
randomForest) for a binary predictor would give two values, one for each category. My question is: how exactly do you interpret those values? The documentation of
partialPlot is quite cryptic in this respect.
My confusion arises, I guess, because I'm used with usual logistic regression where with a dummy coding system you in general obtain the log-odds of the variable of interest against the baseline category. But with RF things are different... Any help is appreciable!