Beyond the Illusion: Berkson's Paradox Unveiled in the Realm of Machine Learning

Abstract

Berkson's Paradox, a statistical phenomenon originally identified in epidemiology, has critical implications in machine learning. In this context, the paradox emerges when using biased or non-representative datasets, leading to distorted model outcomes. When features are selected based on their relationship with the outcome, inherent correlations between predictors may mislead algorithms. This paradox challenges conventional assumptions about variable independence, impacting model generalization and robustness. Recognizing and mitigating Berkson's Paradox is crucial for developing accurate and reliable machine learning models, urging practitioners to carefully evaluate dataset biases and address confounding factors for improved predictive performance in diverse real-world scenarios.