This question already has an answer here:
My question is similar to this link Creating a "certainty score" from the votes in random forests?
I am trying to build a random forest for a binary response (1 & 0). Let's say we have 10,000 different records and I am building 500 trees. Is there a way to score the records in terms of the certainty / confidence / likelihood of being categorized in category 1 (for example)? The link above suggests using the number of votes among all 500 trees, but this way can only give me up to 500 different scores, how can I differentiate further for these 10,000 records? (Like regression, the scores can be easily obtained).
One solution is to average the score of each tree in the forest. the tree is the probability of 1s in the final node. Anyone know how to produce that average in R? I couldnt find this in the randomForest package. I think if I write my own codes to do that it , the run time may not be as fast as a built-in function.