koho 1.1.0

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

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.