  There is an increasing interest in leveraging Large Language Models (LLMs)
for managing structured data and enhancing data science processes. Despite the
potential benefits, this integration poses significant questions regarding
their reliability and decision-making methodologies. It highlights the
importance of various factors in the model selection process, including the
nature of the data, problem type, performance metrics, computational resources,
interpretability vs accuracy, assumptions about data, and ethical
considerations. Our objective is to elucidate and express the factors and
assumptions guiding GPT-4's model selection recommendations. We employ a
variability model to depict these factors and use toy datasets to evaluate both
the model and the implementation of the identified heuristics. By contrasting
these outcomes with heuristics from other platforms, our aim is to determine
the effectiveness and distinctiveness of GPT-4's methodology. This research is
committed to advancing our comprehension of AI decision-making processes,
especially in the realm of model selection within data science. Our efforts are
directed towards creating AI systems that are more transparent and
comprehensible, contributing to a more responsible and efficient practice in
data science.
