Github 项目推荐 | 基于 ID3 算法的 ML 决策树的实现

AI研习社

发布于 2018-07-26 14:04:33

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发布于 2018-07-26 14:04:33

文章被收录于专栏：AI研习社AI研习社

本库是实现用于决策树学习的 ID3 算法的 Ruby 库，目前能够学习连续和离散的数据集。

Github 链接：

https://github.com/igrigorik/decisiontree

特点

用于连续和离散情况的 ID3 算法，支持不一致的数据集。
Graphviz 组件可视化学习树
支持多个符号输出和连续树形图。
当没有分支适合输入时返回默认值

实现

Ruleset 是一个用 2/3 训练数据训练 ID3Tree 的类，并将其转换为一组规则，然后用剩下的 1/3 数据（以 C4.5 的方式，https://en.wikipedia.org/wiki/C4.5_algorithm）修剪规则。

Bagging 是一个基于 Bagging 的训练器，它可以训练 10 个 Ruleset 训练器，并通过投票预测最佳的输出结果。

详细信息请访问以下链接：

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

示例

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

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原始发表：2018-05-03，如有侵权请联系 cloudcommunity@tencent.com 删除

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