something similar to permutation accuracy importance in h2o package

Question

I fitted a random forest for my multinomial target with the randomForest package in R. Looking for the variable importance I found out permutation accuracy importance which is what I was looking for my analysis. I fitted a random forest with the h2o package too, but the only measures it shows me are relative_importance, scaled_importance, percentage.

My question is: can I extract a measure that shows me the level of the target which better classify the variable i want to take in exam? Permutation accuracy importance is the best measure I can use in this case?

For example: I have a 3 levels target: A-B-C and 5 variables: v1-v2-v3-v4-v5 Is there a measure that shows me that v1 is more important for the level A of the target rather than level B (something similiar to the permutation accuracy importance)?

Answer 1

While h2o doesn't provide permutation accuracy importance (as you pointed out it provides variable importance) through the r/python api, you can use PDP h2o.partialPlot() to see how individual levels within a feature impact the target.