decision tree

We have hosted the application decision tree in order to run this application in our online workstations with Wine or directly.

Quick description about decision tree:

DecisionTree is a Ruby library that implements decision tree learning with the ID3 information-gain algorithm. It can train models from discrete, continuous, or mixed attribute data. Continuous features are evaluated across possible split points to build threshold-based binary branches. 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.

Features:

• ID3 learning for continuous and discrete datasets
• Mixed continuous and categorical attributes
• Graphviz tree visualization and PNG export
• Rule generation and C4.5-style pruning
• Ten-model bagging with prediction voting
• Default predictions for unmatched inputs

Programming Language: Ruby.
Categories: