What is the appropriate evaluation metric for RandomForest with probability in R? In order to build a predict model with two categories (buy or not buy),I want to use RandomForest and predict with type='prob', so I can have a prob of someone buy or not buy. So, with this outcome I can clusterize and make groups, like this:

group A: costumer who has [100 to 80]% of buy.
group B: costumer who has [81 to 60]% of buy.
...

But I don't know the appropriate evaluation metric to measure the accuracy of this model. I guess that I can't use a confusion matrix.

Maybe I can use a ROC curve, and or measure KS between the buy group with the not buy group. But I'm not sure about this metrics.
Group the entires by the probability score you assigned them (e.g. 100-95%, 95-90%,....) and then measure the likelihood of their purchases from ground truth.  You can then perform a Chi-squared calculation between your predicted and measured outcomes.