# 决策树

## 基础入门

1.决策树-sklearn库官方介绍英文版 作者：开源库 http://scikit-learn.org/stable/modules/tree.html

2.scikit-learn 0.18 中文文档 作者：ApacheCN Apache中文网 http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030181

3.决策树算法及实践 作者：王大宝的CD http://blog.csdn.net/sinat_22594309/article/details/59090895

4.决策树DTC数据分析及鸢尾数据集分析 作者：Eastmount http://blog.csdn.net/eastmount/article/details/52820400

5.算法杂货铺——分类算法之决策树(Decision tree) 作者：T2噬菌体 http://www.cnblogs.com/leoo2sk/archive/2010/09/19/decision-tree.html

6.机器学习算法之决策树 作者：两棵橘树 http://www.jianshu.com/p/6eecdeee5012

7.决策树 作者：bigbigship http://blog.csdn.net/bigbigship/article/details/50991370

8.机器学习的算法(1):决策树之随机森林 作者：LeftNotEasy http://database.51cto.com/art/201407/444788.htm

9.决策树算法原理(上) 作者：刘建平Pinard http://www.cnblogs.com/pinard/p/6050306.html

1. 决策树算法原理(下) 作者：刘建平Pinard http://www.cnblogs.com/pinard/p/6053344.html

13.学习笔记--算法模型（决策树 作者： happybear https://www.douban.com/note/425127655/

## 代码

1. 机器学习经典算法详解及Python实现--决策树（Decision Tree） [http://blog.csdn.net/suipingsp/article/details/41927247]

2. python实现决策树C4.5算法（数据挖掘算法系列之三） [http://www.clogos.net/cs/datamining/815.html]

3. 1.10. Decision Trees¶ [http://scikit-learn.org/stable/modules/tree.html]

4. 机器学习之决策树（Decision Tree）及其 Python 代码实现 [https://juejin.im/entry/58ba32981b69e6006b15ce67]

### VIP内容

• 最近邻导论
• 决策树集成
• 线性回归线性分类
• Softmax回归、SVM、Boosting
• PCA、Kmeans、最大似然
• 概率图模型
• 期望最大化
• 神经网络
• 卷积神经网络
• 强化学习
• 可微分隐私
• 算法公平性

https://www.cs.toronto.edu/~huang/courses/csc2515_2020f/

### 最新论文

We extend the definitions of complexity measures of functions to domains such as the symmetric group. The complexity measures we consider include degree, approximate degree, decision tree complexity, sensitivity, block sensitivity, and a few others. We show that these complexity measures are polynomially related for the symmetric group and for many other domains. To show that all measures but sensitivity are polynomially related, we generalize classical arguments of Nisan and others. To add sensitivity to the mix, we reduce to Huang's sensitivity theorem using "pseudo-characters", which witness the degree of a function. Using similar ideas, we extend the characterization of Boolean degree 1 functions on the symmetric group due to Ellis, Friedgut and Pilpel to the perfect matching scheme. As another application of our ideas, we simplify the characterization of maximum-size \$t\$-intersecting families in the symmetric group and the perfect matching scheme.

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