Step Details
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| Introduced in Version | Process Mining 3.2 |
| Last Modified in Version | Process Mining 3.2
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| Location | Process Mining > Predictions |
The Get Predictive Objective Functions step in the Decisions Flow toolbox lets users quickly retrieve a comprehensive set of performance metrics. Unlike some steps that require input, this step returns a standardized list of all available evaluation metrics
Properties
Outputs
| Property | Description | Data Type |
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| GetPredictiveModelMeasures_Output | A set of model evaluation metrics:- Accuracy: Proportion of total predictions that are correct.
- Precision: Proportion of positive identifications that were actually correct.
- Recall: Proportion of actual positives correctly identified
- F-Score: Harmonic mean of precision and recall.
- AUC: Area under the ROC curve.
- True Positive: Number of correctly predicted positive cases
- True Negative: Number of correctly predicted negative cases
- False Positive: Number of negative cases incorrectly predicted as positive
- False Negative: Number of positive cases incorrectly predicted as negative
- MEAN_ABSOLUTE_ERROR (MAE): Average absolute difference between predicted and actual values.
- MEAN_ABSOLUTE_PERCENTAGE_ERROR (MAPE): Average absolute percentage difference between predicted and actual values.
- MEAN_SQUARED_ERROR (MSE): Average squared difference between predicted and actual values.
- ROOT_MEAN_SQUARED_ERROR (RMSE): Square root of the average squared differences between predicted and actual values.
- TOTAL_TP: The total number of true positive predictions made by the model.
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