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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

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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.