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

Use the results to compare how well models perform with different settings for the hyperparameters. Click Tune Hyperparameters to Identify a Better Model to evaluate additional values of the hyperparameters.

The optimal number of trees usually differs at each step. When the optimal number is close to the maximum number of trees for the analysis, the model is more likely to improve if you increase the number of trees than a model with an optimal number of trees that is far from the maximum. You can consider whether to further explore an alternative model that seems likely to improve.

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.

When you use the squared error loss function or the Huber loss function,
then the table includes the R^{2} value for each model. The results
that follow are for the model with the highest R^{2} value.

The mean absolute deviation (MAD) is the average of the absolute value of
the difference between a predicted value an actual value. The smaller the MAD,
the better the model fits the data. The 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}.

When you use the absolute deviation loss function, then the table includes the MAD value for each model. The full results that follow the table are for the model with the least MAD value.

Low learning rates weigh each new tree in the model less than higher learning rates and sometimes produce more trees for the model. A model with a low learning rate has less chance of overfitting the training data set. Models with low learning rates generally use more trees to find the optimal number of trees.

The subsample fraction is the proportion of the data that the analysis uses to build each tree.

TreeNet^{®}
Regression
combines many small CART® trees into a powerful model. You can specify either
the maximum number of terminal nodes or the maximum tree depth for these
smaller CART® trees. Trees with more terminal nodes can model more complex
interactions. In general, values above 12 could slow the analysis without much
benefit to the model.

TreeNet^{®}
Regression
combines many small CART® trees into a powerful model. You can specify either
the maximum number of terminal nodes or the maximum tree depth for these
smaller CART® trees. Deeper trees can model more complex interactions. Values
from 4 to 6 are adequate for many datasets.