[FEAT_REQ] Improve Documentation on Supported Machine Learning Models, Datasets, Loss Functions, and Metrics

[tomonarifeehan]

Is your feature request related to a problem? Please describe.
The current documentation lacks clear and comprehensive information on the specific machine learning models, benchmark datasets, loss functions, and evaluation metrics that are supported by our platform. This lack of detailed guidance makes it difficult for users to understand the capabilities of the system, select appropriate models and configurations, and ensure that they are following best practices for their specific use cases. This gap in documentation can lead to confusion, misconfiguration, and increased onboarding time for new users.

Describe the solution you'd like
Improve the documentation by providing a detailed list of all supported machine learning models, including version information and any relevant configuration options. Additionally, include a comprehensive overview of the available benchmark datasets, specifying their sources, use cases, and any preprocessing requirements. Clearly document the supported loss functions and metrics, including explanations of when and why each should be used. This expanded documentation should be easily accessible, regularly updated, and include examples of how to implement and utilize each component within the platform.

Describe alternatives you’ve considered

• Providing minimal updates to the existing documentation with links to external resources, although this would not provide the clear, consolidated reference that users need.
• Relying on inline code comments or tooltips within the UI to describe supported models, datasets, and metrics, but this would not provide the depth of information that comprehensive documentation can offer.

Additional context
Enhancing the documentation will significantly improve the user experience by making it easier for users to leverage the full capabilities of our platform, reduce errors, and ensure that their machine learning workflows are optimized. This improvement would also help in maintaining clarity as new models, datasets, loss functions, and metrics are added. Below are some examples of well-documented machine learning platforms that effectively communicate their supported components.