# What happens to the left over unpicked data in Random Forest

I believe in Random forest we pick random samples of training data with replacement. My question is there still is a possibility that we might leave some data out. What happens to that. Does it not affect the random forest training? Do we use that left over later at some point.

Looking at Scikit-Learn's RandomForestClassifier documentation, we can see that there is a bootstrap argument that can be set to False to ensure all data points are used to fit each of the trees. Otherwise, say you pick some arguments to all be 1 (num_estimators, max_depth, min_samples), then not much data would be used at all! Looking through the source code, there doens't seem to be a check that all data was used.
• A couple of points to add. First, each tree will miss about $1/e$ of the data. But then, with 1,2,3,4 trees you expect to completely ignore 37%, 14%, 5%, 2% of the data respectively. With 10 trees it's down to 0.0045%, with 100 trees $3\cdot10^{-42}$%. So for most applications, you're not missing out on much. Second, any missed samples will show up in all the out-of-bag (OOB) scoring estimates, so not necessarily completely wasted. – Ben Reiniger Apr 26 '19 at 19:35