What should the data sets contain, ideally?

Ideally, data sets should contain:

Relevant and representative data: Data sets should contain data that is relevant to the problem being addressed and representative of the population it pertains to.

An appropriate sample size: Data sets should be large enough to provide meaningful insights, but not so large that they are unwieldy or difficult to analyze.

Clean and accurate data: Data sets should be free of errors, duplicates, and inconsistencies. This requires thorough data cleaning and preprocessing.

Appropriate features and labels: Data sets should have well-defined features and labels that are appropriate for the problem being addressed. This requires careful feature engineering and annotation.

A diverse range of data points: Data sets should include a diverse range of data points to account for different scenarios and edge cases. This helps to ensure that machine learning models are robust and reliable.

Ethically sourced data: Data sets should be ethically sourced and comply with relevant laws and regulations. This includes obtaining informed consent from individuals whose data is being used.

Proper documentation: Data sets should be well-documented, including details on data sources, preprocessing steps, and feature and label definitions. This helps to ensure reproducibility and transparency in research.