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 inconsistent datasets.
Graphviz component to visualize the learned tree
Support for multiple, and symbolic outputs and graphing of continuous 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 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

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

# Specify type ("discrete" or "continuous") in the training data
labels = ["hunger", "color"]
training = [
        [8, "red", "angry"],
        [6, "red", "angry"],
        [7, "red", "angry"],
        [7, "blue", "not angry"],
        [2, "red", "not angry"],
        [3, "blue", "not angry"],
        [2, "blue", "not angry"],
        [1, "red", "not angry"]
]

dec_tree = DecisionTree::ID3Tree.new(labels, training, "not angry", color: :discrete, hunger: :continuous)
dec_tree.train

test = [7, "red", "angry"]
decision = dec_tree.predict(test)
puts "Predicted: #{decision} ... True decision: #{test.last}"

# => Predicted: angry ... True decision: angry

igrigorik/decisiontree

Decision Tree

Features

Implementation

Example

License