Assessing the Effectiveness of Cross-Validation in Evaluating Model Generalizability and Predictive Power

Posted: Mar 05, 2019

• Sophia Hill
• Matthew Jones
• Harper Torres

Abstract

Cross-validation stands as one of the most widely employed techniques in machine learning for estimating model performance and generalization capability. The fundamental premise of cross-validation involves partitioning available data into training and validation subsets to simulate the model's performance on unseen data. Despite its pervasive adoption across academic research and industrial applications, the methodological foundations of cross-validation warrant critical examination regarding its effectiveness in accurately capturing true model generalizability and predictive power. The conventional wisdom surrounding cross-validation assumes that performance estimates derived through repeated data partitioning provide reliable indicators of how models will perform in real-world deployment scenarios. However, this assumption rests on several implicit premises about data characteristics and distributional properties that may not hold in practical applications. This research addresses a significant gap in the current understanding of cross-validation methodologies by systematically investigating their effectiveness across diverse data environments and application contexts. The novelty of our approach lies in the development of a comprehensive multi-dimensional assessment framework that evaluates cross-validation performance beyond traditional accuracy metrics. We introduce three critical dimensions of evaluation: data distribution sensitivity, which examines how cross-validation estimates vary with changes in underlying data distributions; temporal stability, which assesses the reliability of cross-validation in time-dependent data scenarios; and domain adaptation capability, which measures how well cross-validation predicts performance across different domains or contexts.