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-= Decision Tree
+# 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
+## Features
 - ID3 algorithms for continuous and discrete cases, with support for incosistent datasets.
 - Graphviz component to visualize the learned tree (http://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 
+## 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: http://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/
 
-== Example
+## Example
 
-  require 'decisiontree'
+```ruby
+require 'decisiontree'
 
-  attributes = ['Temperature']
-  training = [
-    [36.6, 'healthy'],
-    [37, 'sick'],
-    [38, 'sick'],
-    [36.7, 'healthy'],
-    [40, 'sick'],
-    [50, 'really sick'],
-  ]
+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
+# 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([37, 'sick'])
+puts "Predicted: #{decision} ... True decision: #{test.last}";
 
-  decision = dec_tree.predict(test)
-  puts "Predicted: #{decision} ... True decision: #{test.last}";
+# => Predicted: sick ... True decision: sick
+```
 
-  => Predicted: sick ... True decision: sick
\ No newline at end of file
+## License
+
+The MIT License - Copyright (c) 2006 Ilya Grigorik