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 Optimization
of Hyperparameters table.

After the creation of a model with
Discover Best Model
(Binary Response),
you can click
Select an Alternative Model
to explore other models. If you select a Random Forests
^{®} model, one option is to specify hyperparameters to fit multiple
new models. If you specify hyperparameters, then the results include the
Optimization of Hyperparameters table. The table compares the combinations of
hyperparameters. The results that follow the Optimization of Hyperparameters
table are for the model with the best value of the optimality criterion, such
as the minimum average –loglikelihood.

The average –loglikelihood is a measure of model accuracy. Smaller values indicate a better fit.

The ROC curve plots the True Positive Rate (TPR), also known as power, on the y-axis. The ROC curve plots the False Positive Rate (FPR), also known as type 1 error, on the x-axis. The area under an ROC curve indicates whether the model is a good classifier.

The area under the ROC curve values typically range from 0.5 to 1. Larger values indicate a better classification model. When the model can perfectly separate the classes, then the area under the curve is 1. When the model cannot separate the classes better than a random assignment, then the area under the curve is 0.5.

The misclassification rate indicates how often the model accurately classifies the response values. Smaller values indicate better performance.

This row indicates the choice for the number of predictors to consider.

The minimum internal node size indicates the minimum number of cases a node can have and still split into more nodes.

The number of bootstrap samples indicates the number of trees in the analysis.