A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time
Hoeffding trees exploit the fact that a small sample can often be enough to choose an optimal splitting attribute.This idea is supported mathematically by the Hoeffding bound, which quantifies the number of observations (in our case, examples) needed to estimate some statistics within a prescribed precision (in our case, the goodness of an attribute).
A theoretically appealing feature of Hoeffding Trees not shared by otherincremental decision tree learners is that it has sound guarantees of performance.
Using the Hoeffding bound one can show that its output is asymptotically nearly identical to that of a non-incremental learner using infinitely many examples.For more information see:
Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams.
In: ACM SIGKDD Intl.Conf.
on Knowledge Discovery and Data Mining, 97-106, 2001.
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