Beyond Accuracy: Understanding the Risks of Excessive Features in Regression Models

While it may seem logical that incorporating more features into a regression model would enhance its predictive capabilities, this approach can inadvertently introduce significant risks. Each additional feature increases reliance on data pipelines, external systems, and the integrity of data quality checks. A single missing variable or a change in data structure can lead to a decline in model performance in real-world applications. The core issue lies not in the computational demands or complexity of the system, but in the instability of the model's weights. In regression analysis, particularly with correlated or weakly informative features, the model may struggle to accurately assign influence, resulting in unpredictable shifts in coefficients. This can lead to a façade of sophistication in the model while it performs inconsistently in practice. This article delves into the reasons why an increase in features can diminish the reliability of regression models. We will analyze how correlated features can skew coefficient estimates, how weak signals can be misinterpreted as significant patterns, and how each added feature contributes to production fragility. Through a case study using a property pricing dataset, we will contrast the performance of complex models with simpler, more robust alternatives.