MAD vs number of trees plot for Fit Model and Discover Key Predictors with TreeNet^® Regression

Note

This command is available with the Predictive Analytics Module. Click here for more information about how to activate the module.

The MAD vs Number of Trees Plot displays the mean absolute deviation on the y-axis and the number of trees on the x-axis. The mean absolute deviation indicates whether the model is a good fit. When the analysis uses a validation method, the plot includes a line for the validation results. Use the validation results to assess the performance of the model to predict new observations. Compare the training results and the validation results to see whether there are overfitting problems with the model for the training data set.

This analysis grows 5000 trees. The optimal number of trees is 4905. The optimal value for the test data when the number of trees is 4905 is approximately 39580.

When the squared error or loss function method determines the number of trees for the optimal model, then Minitab displays the R² vs Number of Trees plot.

Interpretation

Lower MAD values indicate a better model. The reference line indicates the optimal MAD value for the validation results and the number of trees in the model. If the curve from the validation results indicates an insufficient model, consider whether to retry the analysis with alternative settings, such as larger or smaller learning rates, or a larger subsample fraction.

MAD vs number of trees plot for Fit Model and Discover Key Predictors with TreeNet® Regression

Note

Interpretation

MAD vs number of trees plot for Fit Model and Discover Key Predictors with TreeNet^® Regression