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
Categories
Data VisualizationLicense
Apache License V2.0Follow EvoTrees.jl
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