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Mitigating multiple descents: A model agnostic framework for risk monopolization Ratio PatilArun Kumar KuchibhotlaYuting Alessandro Rinaldo 25, 2022 Abstract Recent empirical and theoretical analyses
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A model-agnostic framework is used for assessing and auditing machine learning models without being tied to a specific model architecture. It allows for evaluation and analysis across different types of models.
Organizations and individuals that utilize machine learning models and need to comply with regulatory standards or best practices in data reporting and transparency are required to file a model-agnostic framework.
To fill out a model-agnostic framework, one should gather relevant data about the models being assessed, including performance metrics and model assumptions, and then input this information into the standardized format provided by the regulatory body.
The purpose of a model-agnostic framework is to provide a standardized approach to evaluate the effectiveness, fairness, and transparency of various machine learning models, facilitating comparison and understanding across different technologies.
Information that must be reported includes model performance metrics, data sources, assumptions made during modeling, potential biases, and any relevant validations or tests conducted.
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