"Bi thing" Microsoft Decision Tree algorithm

The Microsoft Decision tree algorithm is a classification and regression algorithm provided by Microsoft SQL Server Analysis Services for predictive modeling of discrete and continuous attributes.
For discrete attributes, the algorithm predicts the relationships between the input columns in the dataset. It uses the values of these columns (also called states) to predict the state of a column that is specified as predictable. Specifically, the algorithm identifies the input columns that are related to the predictable column. For example, in the scenario of predicting which customers might purchase a bicycle, if nine of the 10 young customers purchased bicycles, but only two of the 10 older customers purchased bicycles, the algorithm infers that age is the best predictor of bike buying. The decision tree roots are predicted according to the trend toward specific results.
For continuous attributes, the algorithm uses linear regression to determine the split position of the decision tree.
If more than one column is set as a predictable column, or if the input data contains nested tables that are set to be predictable, the algorithm generates a separate decision tree for each predictable column.

Example
Adventure Works Cycles's marketing department wants to identify some of the characteristics of previous customers, which may indicate whether these customers will be able to purchase their products in the future. The ADVENTUREWORKS2008R2 database store describes demographic information about its previous customers. By using the Microsoft decision tree algorithm to analyze this information, the marketing department can generate a model that predicts whether a customer will purchase a product based on the state of a known column about a particular customer, such as demographics or previous purchase patterns.
Here we go to the topic, through a simple process configuration we can implement the entire data mining process, followed by the following steps:

"Bi thing" Microsoft Decision Tree algorithm