Complete example of decision tree algorithm implemented in Java

This paper describes the decision tree algorithm implemented in Java. Share with you for your reference, as follows:

Decision tree algorithm is a method to approximate the value of discrete function. It is a typical classification method. Firstly, the data is processed, and the inductive algorithm is used to generate readable rules and decision trees, and then the decision is used to analyze the new data. In essence, decision tree is a process of classifying data through a series of rules.

The construction of decision tree can be divided into two steps. The first step is the generation of decision tree: the process of generating decision tree from training sample set. Generally, the training sample data set is a historical and comprehensive data set used for data analysis and processing according to the actual needs. The second step is decision tree pruning: decision tree pruning is the process of checking, correcting and repairing the decision tree generated in the previous stage. It mainly uses the data in the new sample data set (called test data set) to verify the preliminary rules generated in the decision tree generation process and cut off those branches that affect the balance accuracy.

The Java implementation code is as follows:

Operation results:

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