From Logistic Regression to AI (www.johndcook.com)

Recent discussions highlight the parallels between logistic regression and neural networks, particularly large language models (LLMs). While logistic regression excels with small datasets, neural networks can leverage vast amounts of data and parameters, leading to emergent phenomena that challenge traditional statistical beliefs. The article underscores how, in small data scenarios, every parameter is critical, with an often-quoted guideline suggesting a minimum of 10 events per parameter. Contrastingly, LLMs thrive on an overwhelming number of parameters, raising questions about the efficacy of model over-parameterization. The significance lies in understanding the scaling laws that govern model performance and parameter efficacy. Interestingly, the data-to-parameter ratios in both logistic regression and LLMs show surprising similarities, suggesting a common underlying principle despite their operational scales. The piece explores the implications of these findings for the AI/ML community, emphasizing the need to rethink classical statistical rules in the light of modern advancements like LLMs, where intuition about data efficiency may no longer apply. This shift prompts a closer examination of how we approach model design and validation in both classical and contemporary machine learning frameworks.