基於 ID3 演算法的 ML 決策樹的實現

本庫是實現用於決策樹學習的 ID3 演算法的 Ruby 庫，目前能夠學習連續和離散的數據集。

Github 鏈接：

https://github.com/igrigorik/decisiontree

特點

用於連續和離散情況的 ID3 演算法，支持不一致的數據集。

Graphviz 組件可視化學習樹

支持多個符號輸出和連續樹形圖。

當沒有分支適合輸入時返回默認值

實現

Bagging 是一個基於 Bagging 的訓練器，它可以訓練 10 個 Ruleset 訓練器，並通過投票預測最佳的輸出結果。

詳細信息請訪問以下鏈接：

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:#... True decision:#"

# => 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:#... True decision:#"

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

從Python入門-如何成為AI工程師

BAT資深演算法工程師獨家研發課程

最貼近生活與工作的好玩實操項目

班級管理助學搭配專業的助教答疑

學以致用拿offer，學完即推薦就業

新人福利

關注 AI 研習社（okweiwu），回復1領取

【超過 1000G 神經網路 / AI / 大數據資料】

開發者自述：我是如何理解決策樹的

喜歡這篇文章嗎？立刻分享出去讓更多人知道吧！