= Decision Tree



A ruby library which implements ID3 (information gain) algorithm for decision tree learning. Currently, continuous and discrete datasets can be learned.



- Discrete model assumes unique labels & can be graphed and converted into a png for visual analysis

- Continuous looks at all possible values for a variable and iteratively chooses the best threshold between all possible assignments. This results in a binary tree which is partitioned by the threshold at every step. (e.g. temperate > 20C)



== Features

- ID3 algorithms for continuous and discrete cases, with support for incosistent datasets.

- Graphviz component to visualize the learned tree (http://rockit.sourceforge.net/subprojects/graphr/)

- Support for multiple, and symbolic outputs and graphing of continuos trees.

- Returns default value when no branches are suitable for input



== Implementation 

- Ruleset is a class that trains an ID3Tree with 2/3 of the training data, converts it into a set of rules and prunes the rules with the remaining 1/3 of the training data (in a C4.5 way).

- Bagging is a bagging-based trainer (quite obvious), which trains 10 Ruleset trainers and when predicting chooses the best output based on voting.



Blog post with explanation & examples: http://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/