Baseline models demystified: a practical guide

Baseline models are essential in the machine learning (ML) pipeline, providing a simple yet effective starting point for developing high-quality ML solutions. These models serve as a reference or benchmark, enabling practitioners to measure progress, test assumptions, and identify bugs in a more manageable environment. Although baseline models are not intended as final solutions, they are crucial in understanding the data and informing subsequent iterations of ML development. The process of refining a baseline model can reveal limitations and guide the gradual increase in model complexity as needed. Neglecting baseline models can lead to misconceptions about progress, as illustrated by a case where a complex neural network was outperformed by a simpler logistic regression model. Thus, baseline models not only facilitate a better comprehension of the task at hand but also ensure that efforts are directed towards meaningful advancements in model performance.