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This paper discusses a method for selecting prototypes in the classification setting, focusing on achieving sparsity in samples rather than variables. The approach translates the essence of prototypes into a set cover optimization problem, which is solved approximately. The paper emphasizes the interpretative value of prototypes by applying the method to significant datasets and compares it with existing methods.

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What is prototype selection for interpretable?

Prototype selection for interpretable refers to the process of choosing specific models or methods that provide transparent and understandable outcomes, enabling users to gain insight into how decisions are made.

Who is required to file prototype selection for interpretable?

Organizations or individuals involved in the development and use of interpretable machine learning models may be required to file prototype selection for interpretable in order to comply with regulatory standards.

How to fill out prototype selection for interpretable?

Filling out prototype selection for interpretable typically involves providing detailed descriptions of the selected models, methodologies used, context of application, and justification for their interpretability.

What is the purpose of prototype selection for interpretable?

The purpose of prototype selection for interpretable is to ensure transparency, provide meaningful explanations of decision-making processes, and facilitate trust among stakeholders in artificial intelligence systems.

What information must be reported on prototype selection for interpretable?

Information that must be reported includes the model specifications, use case, performance metrics, interpretability features, and potential ethical implications of the decisions made by the model.

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