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

Find definitions and interpretation guidance for the Model summary
table.

Minitab displays results for both the training and test data set. The test results indicate whether the model can adequately predict the response values for new observations, or properly summarize the relationships between the response and the predictor variables. Use the training results to evaluate overfitting of the model.

The number of total predictors available for the TreeNet® model. The total is the sum of the continuous and categorical predictors that you specify.

The number of important predictors in the TreeNet® model. Important predictors have importance scores greater than 0.0. You can use the Relative Variable Importance chart to display the order of relative variable importance. For instance, suppose 10 of 20 predictors are important in the model, the Relative Variable Importance chart displays the variables in importance order.

By default, Minitab grows 300 small CART® trees to produce the TreeNet® model. While this value works well for exploration of the data, consider whether to grow more trees to produce a final model. To change the number of trees grown, go to the Options subdialog box.

The optimal number of trees corresponds to the highest R^{2} value
or the lowest MAD value.

When the optimal number of trees is close to the maximum number of trees that the model grows, consider an analysis with more trees. Thus, if you grow 300 trees and the optimal number comes back as 298, then re-build the model with more trees. If the optimal number continues to be close to the maximum number, continue to increase the number of trees.

R^{2} is the percentage of variation in the response that the model
explains. Outliers have a greater effect on R^{2} than on MAD and MAPE.

When you use a validation method, the table includes an R^{2}
statistic for the training data set and an R^{2} statistic for the test
data set. When the validation method is k-fold cross-validation, the test data
set is each fold when the model building excludes that fold. The test
R^{2} statistic is typically a better measure of how the model works
for new data.

Use R^{2} to determine how well the model fits your data. The
higher the R^{2} value, the better the model fits your data.
R^{2} is always between 0% and 100%.

You can graphically illustrate the meaning of different R^{2}
values. The first plot illustrates a simple regression model that explains
85.5% of the variation in the response. The second plot illustrates a model
that explains 22.6% of the variation in the response. The more variation that
is explained by the model, the closer the data points fall to the fitted
values. Theoretically, if a model can explain 100% of the variation, the fitted
values would always equal the observed values and all of the data points would
fall on the line y = x.

A test R^{2} that is substantially less than the training
R^{2} indicates that the model might not predict the response values
for new cases as well as the model fits the current data set.

The root mean square error (RMSE) measures the accuracy of the model. Outliers have a greater effect on RMSE than on MAD and MAPE.

When you use a validation method, the table includes an RMSE statistic for the training data set and an RMSE statistic for the test data set. When the validation method is k-fold cross-validation, the test data set is each fold when the model building excludes that fold. The test RMSE statistic is typically a better measure of how the model works for new data.

Use to compare the fits of different models. Smaller values indicate a better fit. A test RMSE that is substantially more than the training RMSE indicates that the model might not predict the response values for new cases as well as the model fits the current data set.

The mean square error (MSE) measures the accuracy of the model. Outliers have a greater effect on MSE than on MAD and MAPE.

When you use a validation method, the table includes an MSE statistic for the training data set and an MSE statistic for the test data set. When the validation method is k-fold cross-validation, the test data set is each fold when the model building excludes that fold. The test MSE statistic is typically a better measure of how the model works for new data.

Use to compare the fits of different models. Smaller values indicate a better fit. A test MSE that is substantially more than the training MSE indicates that the model might not predict the response values for new cases as well as the model fits the current data set.

The mean absolute deviation (MAD) expresses accuracy in the same units as
the data, which helps conceptualize the amount of error. Outliers have less of
an effect on MAD than on R^{2}, RMSE, and MSE.

When you use a validation method, the table includes an MAD statistic for the training data set and an MAD statistic for the test data set. When the validation method is k-fold cross-validation, the test data set is each fold when the model building excludes that fold. The test MAD statistic is typically a better measure of how the model works for new data.

Use to compare the fits of different models. Smaller values indicate a better fit. A test MAD that is substantially more than the training MAD indicates that the model might not predict the response values for new cases as well as the model fits the current data set.

The mean absolute percent error (MAPE) expresses accuracy as a percentage of
the error. Because the MAPE is a percentage, it can be easier to understand
than the other accuracy measure statistics. For example, if the MAPE, on
average, is 0.05, then the average ratio between the fitted error and the
actual value across all cases is 5%. Outliers have less of an effect on MAPE
than on R^{2}, RMSE, and MSE.

However, sometimes you may see a very large MAPE value even though the model appears to fit the data well. Examine the fitted vs actual response value plot to see if any data values are close to 0. Because MAPE divides the absolute error by the actual data, values close to 0 can greatly inflate the MAPE.

When you use a validation method, the table includes an MAPE statistic for the training data set and an MAPE statistic for the test data set. When the validation method is k-fold cross-validation, the test data set is each fold when the model building excludes that fold. The test MAPE statistic is typically a better measure of how the model works for new data.

Use to compare the fits of different models. Smaller values indicate a better fit. A test MAPE that is substantially more than the training MAPE indicates that the model might not predict the response values for new cases as well as the model fits the current data set.