Impact of subsampling and pruning on random forests

14 Mar 2016Roxane DurouxErwan Scornet

Random forests are ensemble learning methods introduced by Breiman (2001) that operate by averaging several decision trees built on a randomly selected subspace of the data set. Despite their widespread use in practice, the respective roles of the different mechanisms at work in Breiman's forests are not yet fully understood, neither is the tuning of the corresponding parameters... (read more)

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