# Machine Learning-经典模型之DT Learning

Decision tree learning uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).

• Classification tree analysis is when the predicted outcome is the class to which the data belongs.
• Regression tree analysis is when the predicted outcome can be considered a real number (e.g. the price of a house, or a patient's length of stay in a hospital).

Decision tree learning is the construction of a decision tree from class-labeled training tuples. A decision tree is a flow-chart-like structure, where each internal (non-leaf) node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node.

• ID3 (Iterative Dichotomiser 3)
• C4.5 (successor of ID3)
• CART (Classification And Regression Tree)
• CHAID (CHi-squared Automatic Interaction Detector). Performs multi-level splits when computing classification trees.
• MARS: extends decision trees to handle numerical data better.
• Conditional Inference Trees. Statistics-based approach that uses non-parametric tests as splitting criteria, corrected for multiple testing to avoid overfitting. This approach results in unbiased predictor selection and does not require pruning.

#### 在使用CART方法时，按照集合中子集标签的概率分布对集合中元素指定标签，基尼不纯度用来衡量被错误指定标签的元素被随机抽取的概率；

##### 4>> 对N1N1和N2N2分别继续执行2-3步，直到每个结点足够纯为止；

1）维基百科 https://en.wikipedia.org/wiki/Decision_tree_learning

2）<机器学习笔记-05 ><scikit-learn 05>决策树 & 随机森林

https://blog.csdn.net/qq_25040013/article/details/52565414

3）机器学习笔记之信息熵、信息增益和决策树(ID3算法)

https://blog.csdn.net/zhurui_idea/article/details/54646932

4）Python3《机器学习实战》学习笔记（二）：决策树基础篇之让我们从相亲说起

https://blog.csdn.net/c406495762/article/details/75663451

5）决策树算法及python实现

https://blog.csdn.net/huahuazhu/article/details/73167610?locationNum=2&fps=1

6）Scikit-learn中的决策树

http://python.jobbole.com/86911/

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