README for decision_tree
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A ruby library which implements ID3 (information gain) algorithm for decision tree learning. Currently, continuous and discrete datasets can be learned.
- Discrete 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)
Currently, graphing works properly only for discrete cases due to a limitation in graphviz code.
Graphviz dependency: http://rockit.sourceforge.net/subprojects/graphr/
Enjoy.
Ilya Grigorik (ilya <at> fortehost DOT com)
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A ruby library which implements ID3 (information gain) algorithm for decision tree learning. Currently, continuous and discrete datasets can be learned.
- Discrete 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)
Currently, graphing works properly only for discrete cases due to a limitation in graphviz code.
Graphviz dependency: http://rockit.sourceforge.net/subprojects/graphr/
Enjoy.
Ilya Grigorik (ilya <at> fortehost DOT com)