Randomer Forest (RerF) is a generalization of the Random Forest (RF) algorithm. RF partitions the input (feature) space via a series of recursive binary hyperplanes. Hyperplanes are constrained to be axis-aligned. In other words, each partition is a test of the form X_i > t, where t is a threshold and X_i is one of p inputs (features) {X₁, …, X_p}. The best axis-aligned split is found by sampling a random subset of the p inputs and choosing the one that best partitions the observed data according to some specified split criterion. RerF relaxes the constraint that the splitting hyperplanes must be axis-aligned. That is, each partition in RerF is a test of the form w₁X₁ + … + w_pX_p > t. The orientations of hyperplanes are sampled randomly via a user-specified distribution on the coefficients w_i, although an empirically validated default distribution is provided. Currently only classification is supported. Regression and unsupervised learning will be supported in the future.

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