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 (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: www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/

Example¶ ↑

require 'decisiontree'

attributes = ['Temperature']
training = [
  [36.6, 'healthy'],
  [37, 'sick'],
  [38, 'sick'],
  [36.7, 'healthy'],
  [40, 'sick'],
  [50, 'really sick'],
]

# Instantiate the tree, and train it based on the data (set default to '1')
dec_tree = DecisionTree::ID3Tree.new(attributes, training, 'sick', :continuous)
dec_tree.train

test = [37, 'sick']

decision = dec_tree.predict(test)
puts "Predicted: #{decision} ... True decision: #{test.last}";

=> Predicted: sick ... True decision: sick