Similar to the decision tree classification method, decision tree regression is the application of decision tree model to solve regression problems, that is, it can predict continuous values.

The data Training Procedure of decision tree regression can be used to obtain the model according to the data characteristics, and then used for prediction.

When creating a decision tree regression training task, you need to set the following parameters:

• Training Dataset: required parameter. The Dataset to be trained accesses Connection Info, including Data Type, Connect Parameter, Dataset name, etc. You can connect HBase data, DSF data, and Local Data.

• Data Query Conditions: optional parameter; the specified data can be filtered out for corresponding analysis according to the Query Conditions; attribute conditions and Spatial Query are supported. E.g. SmID < 100 and BBOX(the_geom, 120,30,121,31)。

• Explanatory Fields: a required parameter, the field of the explanatory variable. Enter one or more explanatory fields of the training Dataset as the independent variables of the model, which can help predict the results.

• Modeling field: a required parameter, which is used to train the field of the model, that is, the dependent variable. This field corresponds to a known (trained) value of a variable that will be used to make predictions at unknown locations.

• Maximum depth of the

  tree: An optional parameter, also known as the maximum number of partitions made into the tree. Range of values & gt; 0. The default is 5. If you use a larger maximum depth, more divisions will be created, which may increase the likelihood of overfitting the model.

• Leaf Node Splitting Threshold: Optional parameter, the minimum number of observations required to retain a leaf (i.e., a terminal node on a tree that is not further split). The value range is 0, and the default value is 1. For very large data, increasing the number will reduce the runtime of the tool.

• Distance explanatory variable Dataset: optional parameter, supports point, line and Region Dataset, calculates the Closest distance between the elements of the given Dataset and the elements in the training Dataset, and automatically creates a list of explanatory variables.

• Model Save Directory: optional parameter; save the model with good Training Result to this address. An empty value indicates that the Model will not be saved.

After executing the training task, the following Result Parameter is output:

• dTRModelCharacteristics: Properties of the decision tree regression model.

• Variable: The Field array of the decision tree regression model, which refers to the field of the independent variable in the training model.

• Variable Importances: The field importance refers to the degree of influence of the respective variable characteristics on the dependent variable.

• MSE: mean square error, the mean of the squared error between the predicted value and the true value.

• RMSE: RMSE, the mean of the square root of the error between the predicted value and the true value.

• Mae: mean absolute error, the mean of the absolute value of the error between the predicted value and the true value.

• r2: coefficient of determination. According to the value of r2, the quality of the model can be judged. The value range is [0,1]. Generally speaking, the larger the r2 is, the better the fitting effect of the model is. r2 reflects how accurate it is, because with the increase of the number of samples, r2 will inevitably increase, which can not really quantitatively explain the degree of accuracy, but can only be roughly quantitative.

• explained Variance: explains the variance.