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Decision Forest C++ library with a scikit-learn compatible Python interface

Project description

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koho (TM)

koho (Hawaiian word for ‘to estimate’) is a Decision Forest C++ library with a scikit-learn compatible Python interface.

  • Classification
  • Numerical (dense) data
  • Missing values (Not Missing At Random (NMAR))
  • Class balancing
  • Multi-Class
  • Multi-Output (single model)
  • Build order: depth first
  • Impurity criteria: gini
  • n Decision Trees with soft voting
  • Split a. features: best over k (incl. all) random features
  • Split b. thresholds: 1 random or all thresholds
  • Stop criteria: max depth, (pure, no improvement)
  • Bagging (Bootstrap AGGregatING) with out-of-bag estimates
  • Important Features
  • Export Graph

ReadTheDocs

New BSD License

Change Log: 1.1.0 Multi-Output (single model) 1.0.0 Missing Values (NMAR) : Python, Cython(bindings), C++ 0.0.2 Criterion implemented in Cython 0.0.1 Classification : Python only

Copyright 2019, AI Werkstatt (TM). All rights reserved.

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