---
title: "Get Predictive Objective Functions"
slug: "get-predictive-objective-functions"
updated: 2025-04-21T16:00:23Z
published: 2025-04-21T16:00:23Z
canonical: "documentation.decisions.com/get-predictive-objective-functions"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://documentation.decisions.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Get Predictive Objective Functions

| Step Details |
| --- |
| **Introduced in Version** | Process Mining 3.2 |
| **Last Modified in Version** | Process Mining 3.2 |
| **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 |
| --- | --- | --- |
| 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. | -- |

## ![](https://cdn.document360.io/6ef8bcc1-6489-4486-9ad1-83acff7e5df0/Images/Documentation/image-1745011005226.png)![](https://cdn.document360.io/6ef8bcc1-6489-4486-9ad1-83acff7e5df0/Images/Documentation/image-1745011063686.png)