Decision Tree
ID3-based implementation of the ML Decision Tree algorithm
...Discrete models classify unique labels and can be rendered for visual inspection. The library supports inconsistent datasets, multiple or symbolic outputs, and fallback values when no branch matches an input. Its Ruleset trainer converts a tree into pruned rules using a held-out portion of the training data. A bagging trainer builds ten rulesets and combines their predictions through voting.