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Interpret the results for Discover Best Model (Binary Response)

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

In This Topic

The results include the Model Selection table and the results for the model with the best value of the accuracy criterion for the analysis, such as the minimum average –loglikelihood. Go to the corresponding model type for guidance on the interpretation of the results.

Model Selection

The results for Discover Best Model (Binary Response) include the Model Selection table. Use the results to compare how well the different types of models perform. An asterisk identifies the best model. The table includes the following measures of model performance:
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.
For classification trees, 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.
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