A Julia implementation of boosted trees with CPU and GPU support. Efficient histogram-based algorithms with support for multiple loss functions, including various regressions, multi-classification and Gaussian max likelihood.

Features

  • Data consists of randomly generated Matrix{Float64}
  • Training is performed on 200 iterations
  • Model training is performed using fit_evotree
  • It supports additional keyword arguments to track evaluation metric and perform early stopping
  • When using a DataFrames as input, features with elements types Real (incl. Bool) and Categorical are automatically recognized as input features
  • Returns the normalized gain by feature

Project Samples

EvoTrees.jl Screenshot 1

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