Get the free UTILITY EVALUATION OF MODELS - vis cs ucdavis

UTILITY EVALUATION OF MODELS Jean Schultz, Briana Love, Mark Whiting Pacific Northwest National Laboratory 903 Battle Blvd. Richland, WA 99354 18883757665jean. Schultz, Briana. Love, mark. Whiting@PNL.gov

How to fill out utility evaluation of models

How to fill out utility evaluation of models

1. Start by identifying the utility criteria that are relevant for evaluating models. These criteria could include accuracy, precision, recall, F1 score, computational efficiency, interpretability, etc.
2. For each utility criterion, define a scale or scoring system. This could be a numerical scale from 1 to 10, or a qualitative scale like 'low', 'medium', 'high'.
3. Gather the necessary data or information to evaluate the models. This could include predictions and ground truth labels for a test dataset, computational resource usage measurements, or any other relevant data.
4. Apply the defined scoring system to each utility criterion based on the evaluation data. Calculate the scores for each criterion.
5. Normalize the scores if necessary, to ensure that they are comparable across different models or evaluations.
6. Summarize the scores for each model or evaluation, considering all the utility criteria. This could be done by taking the average score, weighted average, or any other suitable method.
7. Present the utility evaluation results in a clear and understandable manner, such as through tables, graphs, or written summaries.
8. Take into account any limitations or biases in the utility evaluation process, and communicate them along with the results.
9. Regularly update and repeat the utility evaluation as needed, to ensure that models continue to meet the required standards and needs.

Who needs utility evaluation of models?

1. Various stakeholders may need utility evaluation of models, including:
2. - Data scientists and machine learning engineers who develop and improve models, as it helps them assess the performance and effectiveness of different models.
3. - Businesses and organizations that rely on machine learning models for decision-making, as it helps them measure the utility and suitability of these models.
4. - Researchers and academics who study or compare different models, as it provides a standardized way to evaluate and compare their utility.
5. - Regulatory bodies or compliance teams who need to ensure that models meet certain standards or regulations, as it helps them assess the utility and compliance of the models.
6. - End users or customers who use or interact with models, as it helps them understand the reliability and effectiveness of the models in their specific use cases.

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