ModelMetadata

Extracts and summarizes critical metadata from a machine learning model instance for comprehensive analysis.

Purpose

This test is designed to collect and summarize important metadata related to a particular machine learning model. Such metadata includes the model’s architecture (modeling technique), the version and type of modeling framework used, and the programming language the model is written in.

Test Mechanism

The mechanism of this test consists of extracting information from the model instance. It tries to extract the model information such as the modeling technique used, the modeling framework version, and the programming language. It decorates this information into a data frame and returns a summary of the results.

Signs of High Risk

• High risk could be determined by a lack of documentation or inscrutable metadata for the model.
• Unidentifiable language, outdated or unsupported versions of modeling frameworks, or undisclosed model architectures reflect risky situations, as they could hinder future reproducibility, support, and debugging of the model.

Strengths

• Increased transparency and understanding regarding the model’s setup.
• Supports better error understanding and debugging.
• Facilitates model reuse.
• Aids compliance of software policies.
• Assists in planning for model obsolescence due to evolving or discontinuing software and dependencies.

Limitations

• Dependent on the compliance and correctness of information provided by the model or the model developer.
• If the model’s built-in methods for describing its architecture, framework, or language are incorrect or lack necessary information, this test will hold limitations.
• Not designed to directly evaluate the performance or accuracy of the model, rather it provides supplementary information which aids in comprehensive analysis.