Model Generalization Failure Patterns Under Sparse Data Conditions

Abstract

Generalization failure in machine learning models trained under sparse data conditions does not occur
as a single collapse but emerges through a sequence of identifiable failure patterns. This study
characterizes three dominant stages of degradation feature over-amplification, boundary contraction,
and identity collapse and analyzes how each reflects the underlying instability of the model’s
representation space. Using controlled sparsity scaling, representational perturbation tests, and decision
boundary evaluation, we demonstrate that early failure signals appear before accuracy decline, allowing
failure to be detected before deployment-level breakdown occurs. The findings emphasize that
robustness in sparse data environments depends on maintaining representational redundancy and
structural relational cues, rather than simply increasing model size or regularization strength. This work
provides a framework for diagnosing and mitigating generalization failure in low-data regimes,
supporting more stable and reliable machine learning behavior in real-world settings.