Optimization of hyperparameters for Discover Best Model (Binary Response)


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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.

Average –loglikelihood

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

Area under ROC curve

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.

Misclassification rate

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

Predictor count for node splitting

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

Minimum internal node size

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

Number of bootstrap samples

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

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