My question is about binary classification, say separating good customers from bad customers, but not regression or non-binary classification. In this context, a random forest is an ensemble of classification trees. For each observation, every tree votes a "yes" or "no", and the average vote of all trees is the final forest probability.
My question is about modifying the behavior of the underlying trees: How can we modify the randomForest function (of the randomForest package of R) so that each tree votes a decimal instead of a binary yes/no. To better understand what I mean by decimal, let's think about how decision trees work.
A fully grown decision tree has 1 good or 1 bad instance in its terminal nodes. Assume that I limit the terminal node size as 100. Then terminal nodes are going to look like:
Node1 = 80 bad, 20 good
Node2 = 51 bad, 49 good
Node3 = 10 bad, 90 good
Notice, even though Node1 and Node2 vote "bad", their "strength of bad-ness" is severely different. That is what I am after. Instead of having them produce 1 or 0 (which is the default behavior) can one modify the R package so they vote 80/100, 51/100, 10/100 etc?