- OutputTable
- Specify the name for the output table that the function creates. The output_table must not already exist.
- ModelIDColumn
- [Optional] Specify the name of the input table column that contains the model or partition identifiers for the ROC curves.
- ProbabilityColumn
- Specify the name of the input table column that contains the predictions.
- ObsColumn
- Specify the name of the input table column that contains the actual classes.
- PositiveClass
- Specify the label of the positive class.
- NumThreshold
- [Optional] Specify the number of thresholds for the function to use. The num_threshold must be a NUMERIC value in the range [1, 10000].
- ROCValues
- [Optional] Specify whether the function displays ROC values (thresholds, false positive rates, and true positive rates).
- AUC
- [Optional] Specify whether the function displays the AUC calculated from the ROC values.
- Gini
- [Optional] Specify whether the function displays the Gini coefficient calculated from the ROC values. The Gini coefficient is a measure of inequality among values of a frequency distribution. A Gini coefficient of 0 indicates that all values are the same. The closer the Gini coefficient is to 1, the more unequal are the values in the distribution.
The valid combinations of ROCValues, AUC, and Gini arguments are those that specify one of the following:
- ROCValues only
- AUC only
- Gini only
- AUC and Gini
When specifying AUC only, Gini only, or AUC and Gini only, you need not specify ROCValues ('false'), but you must not specify ROCValues ('true').
If you specify an invalid combination (such as ROCValues ('true') and AUC ('true'), or all three 'false'), the function issues an error message.