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)
- 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
- 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
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: angryThe MIT License - Copyright (c) 2006 Ilya Grigorik