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The performance index of the regression prediction algorithm on the validation set.

 

How-To-Use:

The input of this node is the predicted result of the model and the true target value (dependent variable). There are at least two columns of data in the data set.

After setting the performance evaluation node of the regression model, you can view the performance indicators by connecting the table view [MSE (Mean Square Error): It is a measure that reflects the degree of difference between the estimator and the estimator. RMSE (root mean square error): is the square root of the ratio of the square of the deviation between the predicted value and the true value to the number of observations n. MAE (Mean Absolute Error): The average absolute error is the average value of the absolute error, and the average absolute error can better reflect the actual situation of the predicted value error. EVS (Explained Variance): Explain the variance score of the regression model. Its value range is [0,1]. The closer to 1, the more the independent variable can explain the variance of the dependent variable. The smaller the value, the worse the effect. R2 (Goodness of fit): It is an important statistic reflecting the goodness of fit of the model, which is the ratio of the regression sum of squares to the total sum of squares. ]; Connect the picture view to see the comparison between the actual value and the predicted value.

 

Precautions:

[Real Value] and [Predicted Value] can only select one column respectively.

 

❖Configuration

After adding the Regression Evaluation node to the experiment, you can set the Regression Evaluation node through the "Configuration" page on the right.

[Reserved digits of performance index] When the rounding precision is positive, the digits after the decimal point are retained; when the rounding precision is negative, the digits before the decimal point are retained.

[Real Value] Select the true value field of the pre-data node.

[Predicted value] Select the forecast value field in the forecast result.

Right-click menu of regression evaluation:

❖Run Regression Evaluation Node

Run the node, pass the data to DM-Engine for calculation, and get the output result.

 

❖Reset Regression Evaluation Node

The node that has been running is reset, the returned result is deleted, and the node status is changed to not running.

 

❖Rename Regression Evaluation Node

In the right-click menu of the Regression Evaluation node, select "Rename" to rename the node.

 

❖Refresh Regression Evaluation Node

In the right-click menu of the Regression Evaluation node, select "Refresh" to update the synchronization data or parameter information.

 

❖Save as Composite Node

In the right-click menu of the Regression Evaluation node, select "Save as Composite Node",The selected node can be saved as a composite node to realize a multiplexing node, and the parameters of the saved node are consistent with the original node.

 

❖Cut Regression Evaluation Node

In the right-click menu of the Regression Evaluation node, select "Cut" to realize node cutting operation.

 

❖Copy Regression Evaluation Node

In the right-click menu of the Regression Evaluation node, select "Copy" to realize node  replication operation.

 

❖Delete Regression Evaluation Node

In the right-click menu of a Regression Evaluation node, select "Delete" or click the delete key on the keyboard to delete the node and its input and output connections.