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There is an increasing interest in leveraging Large Language Models (LLMs)for managing structured data and enhancing data science processes. Despite thepotential benefits, this integration poses significant questions regardingtheir reliability and decision-making methodologies. It highlights theimportance of various factors in the model selection process, including thenature of the data, problem type, performance metrics, computational resources,interpretability vs accuracy, assumptions about data, and ethicalconsiderations. Our objective is to elucidate and express the factors andassumptions guiding GPT-4's model selection recommendations. We employ avariability model to depict these factors and use toy datasets to evaluate boththe model and the implementation of the identified heuristics. By contrastingthese outcomes with heuristics from other platforms, our aim is to determinethe effectiveness and distinctiveness of GPT-4's methodology. This research iscommitted to advancing our comprehension of AI decision-making processes,especially in the realm of model selection within data science. Our efforts aredirected towards creating AI systems that are more transparent andcomprehensible, contributing to a more responsible and efficient practice indata science.

GPT in Data Science: A Practical Exploration of Model Selection