How Ensemble Learning Balances Accuracy and Overfitting: A Bias-Variance Perspective on Tabular Data

Abstract--Tree-based ensemble methods consistently outperform single models on tabular classification tasks, yet the conditions under which ensembles provide clear advantages--and prevent overfitting despite using high-variance base learners--are not always well understood by practitioners. We study four real-world classification problems (Breast Cancer diagnosis, Heart Disease prediction, Pima Indians Diabetes, and Credit Card Fraud detection) comparing classical single models against nine ensemble methods using five-seed repeated stratified cross-validation with statistical significance testing. Our results reveal three distinct regimes: (i) On nearly linearly separable data (Breast Cancer), well-regularized linear models achieve 97% accuracy with <2% generalization gaps; ensembles match but do not substantially exceed this performance. We systematically quantify dataset complexity through linearity scores, feature correlation, class separability, and noise estimates, explaining why different data regimes favor different model families. Cross-validated train/test accuracy and generalization-gap plots provide simple visual diagnostics for practitioners to assess when ensemble complexity is warranted. Statistical testing confirms that ensemble gains are significant on nonlinear tasks (p < 0.01) but not on near-linear data (p > 0.15). The study provides actionable guidelines for ensemble model selection in high-stakes tabular applications, with full code and reproducible experiments publicly available. A model that almost perfectly fits its training data can still fail badly on new cases. This gap between training performance and real-world behaviour is the essence of overfitting, and it is particularly problematic in domains such as medical diagnosis and financial fraud detection, where mistakes are costly: missed tumours delay treatment, and undetected fraud translates directly into monetary loss.