On the rates of convergence of a kernel based random forest algorithm
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Random forests are notable learning algorithms introduced by Breiman in 2001 and are widely used for classification and regression tasks. In this talk, we consider a specific class of random forest algorithms related to kernel methods: the KeRF (Kernel Random Forests). We introduce the simplified directional KeRF and discuss rates of convergence under different hypotheses. This talk is based on a joint work with N. Arcozzi