Different results using Random Forest prediction in R

Question

When I'm running random forest model over my test data I'm getting different results for the same data set + model .

Here are the results where you can see the difference over the first column:

> table((predict(rfModelsL[[1]],newdata = a)) ,a$earlyR)

        FALSE TRUE
 FALSE    14    7
 TRUE     13   66

> table((predict(rfModelsL[[1]],newdata = a)) ,a$earlyR)

        FALSE TRUE
 FALSE    15    7
 TRUE     12   66

Although the difference is very small, I'm trying to understand what caused that. I'm guessing that predict has "flexible" classification threshold, although I couldn't find that in the documentation; Am I right?

Thank you in advance

Answer 1

I will assume that you did not refit the model here, but it is simply the predict call that is producing these results. The answer is probably this, from ?predict.randomForest :

Any ties are broken at random, so if this is undesirable, avoid it by using odd number ntree in randomForest()