GPT in Data Science: A Practical Exploration of Model Selection
This work addresses the need for more transparent AI decision-making in data science, though it is incremental as it focuses on evaluating an existing model rather than introducing new methods.
The paper tackled the problem of evaluating GPT-4's reliability and methodology in model selection for data science, using a variability model and toy datasets to compare its heuristics with other platforms, finding that it offers distinct but not always superior recommendations.
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.