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Application Process for Accessing VRAGR Datasets Preamble Datasets for the Violence Risk Appraisal Guide Revised (2013) and related projects are stored in the Scholars Portal Dataverse (see https://dataverse.scholarsportal.info/dataset.xhtml?persistentIdhdl:10864/12053&version

How to fill out discrimination and calibration properties

How to fill out discrimination and calibration properties

1. Discrimination and calibration properties can be filled out by first defining the performance metric you want to assess.
2. Next, calculate the discrimination property by comparing the model's predictions for different groups or classes.
3. To fill out the calibration property, you need to calibrate the model's predicted probabilities with the observed outcomes.
4. Finally, document your findings and the steps taken to fill out both discrimination and calibration properties.

Who needs discrimination and calibration properties?

1. Discrimination and calibration properties are needed by data scientists, machine learning engineers, and researchers who want to evaluate the performance of their predictive models.

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What is discrimination and calibration properties?

Discrimination and calibration properties refer to the metrics used to evaluate the performance of a model or system in distinguishing between different classes or categories. Discrimination assesses the ability to correctly identify different classes, while calibration examines how closely the predicted probabilities align with actual outcomes.

Who is required to file discrimination and calibration properties?

Entities that develop predictive models or make decisions based on model outputs, such as financial institutions, healthcare providers, and insurance companies, are typically required to file discrimination and calibration properties to ensure compliance with regulatory standards.

How to fill out discrimination and calibration properties?

To fill out discrimination and calibration properties, one must gather the relevant performance metrics from the model, including AUC-ROC for discrimination and reliability diagrams for calibration. This data should be documented systematically to meet regulatory requirements.

What is the purpose of discrimination and calibration properties?

The purpose of discrimination and calibration properties is to provide insights into the accuracy and reliability of predictive models, ensuring that they perform as intended and do not perpetuate bias or inaccuracy in decision-making processes.

What information must be reported on discrimination and calibration properties?

Information that must be reported includes accuracy measures, such as AUC-ROC, calibration curves, confusion matrices, and any other relevant metrics that demonstrate the model's discrimination and calibration performance.