[Sklearn] Data classification prediction based on decision tree algorithm (Excel can directly replace data)

1. Model Principle

A decision tree is a classification and regression model based on a tree structure, which divides data into different categories or predictive values ​​through a series of decision rules. The model principle and mathematical model of the decision tree are as follows:

1.1 Model principle

The basic idea of ​​a decision tree is to start from the root node, through a series of nodes and branches, divide the data set into different subsets according to the values ​​of different features, until reaching the leaf nodes, and then assign each leaf node to a category or predicted values. The process of building a decision tree is the process of determining how to select features and how to divide the data set.

The main steps of a decision tree:

1. Feature selection: Select the best feature from all features as the partition feature of the current node. This selection is usually based on a certain metric (such as information gain, Gini coefficient) to evaluate the importance of different features.

2. Divide the dataset: according to the selected features &