Easy to understand: the decision tree is represented by intuitive graphics, which is easy to understand and explain. They can help people understand data and decision-making process intuitively, without complicated mathematical background.

Strong interpretability: decision trees provide clear decision paths and rules, so that people can understand how the model makes decisions. This makes the decision tree very useful when it is necessary to explain the model results or make decision interpretation.

Suitable for various types of data: decision trees can be used to process various types of data, including classified data and numerical data. In addition, they can also deal with missing values and abnormal values without complicated preprocessing of data.

Able to handle nonlinear relationships: Decision trees can capture nonlinear relationships and interaction effects well, because they construct decision rules by dividing data spaces, thus allowing the establishment of nonlinear models.

Scalability and efficiency: The process of building and predicting decision trees is usually very efficient. On large-scale data sets, decision tree algorithm can quickly generate models and make predictions.

Robustness to missing values and outliers: decision trees deal with missing values and outliers relatively well. When building a decision tree, different strategies can be used to deal with missing values, and abnormal values usually do not have a great impact on the establishment and prediction of the decision tree.

It should be noted that the decision tree also has some limitations and shortcomings, such as easy over-fitting, sensitive to small changes in input data. In practical application, according to specific problems and data characteristics, the advantages and disadvantages of using decision tree should be comprehensively considered, and other algorithms and technologies should be combined to select and optimize the model.