AMF: Aggregated Mondrian Forests for Online Learning

25 Jun 2019Jaouad MourtadaStéphane GaïffasErwan Scornet

Random Forests (RF) is one of the algorithms of choice in many supervised learning applications, be it classification or regression. The appeal of such tree-ensemble methods comes from a combination of several characteristics: a remarkable accuracy in a variety of tasks, a small number of parameters to tune, robustness with respect to features scaling, a reasonable computational cost for training and prediction, and their suitability in high-dimensional settings... (read more)

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