Categorical Responses | JMP 13.2

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Predictive and Specialized Modeling • Model Comparison • Model Comparison Platform Options • Categorical Responses

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Categorical Responses

Shows ROC curves for each level of the response variable. The curves for the different models are overlaid.

Provides a comparison of the area under the ROC curve (AUC) from each model. The area under the curve is the indicator of the goodness of fit, with 1 being a perfect fit.

The report includes the following information:

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standard errors and confidence intervals for each AUC

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standard errors, confidence intervals, and hypothesis tests for the difference between each pair of AUCs

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an overall hypothesis test for testing whether all AUCs are equal

Shows lift curves for each level of the response variable. The curves for the different models are overlaid.

Cum Gains Curve

Shows cumulative gains curves for each level of the response variable. A cumulative gains curve is a plot of the proportion of a response level that is identified by the model against the proportion of all responses. A cumulative gains curve for a perfect model would reach 1.0 at the overall proportion of the response level. The curves for the different models are overlaid.

Confusion Matrix

Shows confusion matrices for each model. A confusion matrix is a two-way classification of actual and predicted responses. Count and rate confusion matrices are shown. Separate confusion matrices are produced for each level of the Group variable.

If the response has a Profit Matrix column property, then Actual by Decision Count and Actual by Decision Rate matrices are shown to the right of the confusion matrices. For details about these matrices, see Additional Examples of Partitioning in Partition Models.

Shows a profiler for each response based on prediction formula columns in your data. The profilers have a row for each model being compared.

Related Information

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ROC Curve in Partition Models

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Lift Curve in Partition Models

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Help created on 9/19/2017