> For the complete documentation index, see [llms.txt](https://jen-hsuan-hsieh.gitbook.io/python/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jen-hsuan-hsieh.gitbook.io/python/chapter-2courses/21python-for-data-science-and-machine-learning-bootcamp/dsd/21141introduction-of-tree-methods.md).

# 2.1.14.1.Introduction of tree methods

## 1.Introduction

* 會使用決策樹的例子
  * 有一位打球的朋友, 有時會出現而有時不會, 導致他不會出現的因素有很多, 例如天氣, 溫度, 濕度等
  * 於是我們想找出這些因素與最後對方是否會出現
  * 利用這些因素的資料來預測對方是否會出現, 方法之一就是使用決策樹
* 決策樹

    ![](/files/-M4M0SYdmB31kylImFpo)

  * Nodes
    * 因為某Attribute的值而分割 (split)
  * Edges
    * Next node的輸出值
  * Root
    * 第一個split的node
  * Leaves
    * 做預測的**Terminal node**

## 2.分割背後的直覺

* Best split
  * 在資料中最能夠左右結果的因子
* Entropy, information gain
  * 可以協助找出**best split**的數學指標
* Random forest
  * Decision tree的缺點是無法趨向最佳的預測精度 (predictive accuracy)
  * 使用一群隨機在每個split所找出的tree, 特性是變異度高
  * Random force
    * [Bagging](https://zh.wikipedia.org/wiki/Bagging%E7%AE%97%E6%B3%95)是一個常用來降低機器學習算法的變異數的方式, 通常是在所有的隨機樹(p)中挑選m顆
    * 避免單一具領導性的feature造成樹之間的高度相關性

## 3.參考資料

* Refer to Chapter 8. Introduction to Statistical Learning


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