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
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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