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About this paper
Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot, Nathalie Villa-Vialaneix
The authors compare scalable random-forest methods for classification, testing sampling, resampling, divide-and-conquer, and bag-of-little-bootstraps approaches on simulated and real data. In a 15-million-observation simulation, sampled forests and BLB remained close to a sequential reference at a 0.1% sampling fraction. X-biased chunks degraded dacRF predictions and made its approximate OOB diagnostic misleading; results depend on data allocation.
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