Machine learning two--classification algorithm--decision tree DecisionTree

Machine Learning Algorithm Evaluation criteria: accuracy, speed, robustness (less noise impact), scale, and explanatory.

1. Decision Trees Decision Tree: The decision tree is a tree structure similar to a flowchart where each internal node represents a test on an attribute, each branch represents an attribute output, and each leaf node represents the distribution of a class (label) or class. The topmost layer of the tree is the root node.

2, Information entropy: the greater the uncertainty of one thing, the greater the amount of information we need and the greater the entropy. The measurement of information is equal to the amount of uncertainty.

The bit indicates how much information is in H =-∑p (x) logp (x)

Decision tree Induction algorithm chooses attribute judgment node through information entropy calculation:

Information Acquisition (information Gain) Gain (A) =info (d)-info_a (d)

　　　　Amount of information obtained through property A = amount of information required without attribute A-the amount of information required when there is a property a

　　　　

　　　　　　

　　　　　　

By comparing the gain information acquisition of each node, the attribute is determined as the judging node.

3. Algorithm

Machine learning two--classification algorithm--decision tree DecisionTree