Great question — answering as the UphillTech team:
In short: It can be any type of tree inside a forest :) Random forest is an ensemble of many decision trees. The success of a random forest highly depends on using uncorrelated decision trees. If we use the same or very similar trees, the overall result will not be much different from that of a single decision tree. Random forests achieve uncorrelated decision trees by bootstrapping and feature randomness. What kind of decision tree are used in random forest? This depends on implementation. Generally, any bootstrap-aggregated attribute-bagged learner based on trees (any of them) is called Random Forest . You get different flavors using different trees. For example : Function randomForest()...
Full answer by Vineesh Vijayan: https://datascience.stackexchange.com/a/109238 (CC BY-SA 4.0).
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