Supervised Learning Approaches for Robust Predictive Modelling in Data Science

Abstract

Supervised learning remains the dominant paradigm for predictive modeling in data science, yet real-world deployments frequently fail due to fragile data pipelines, distributional shift, and optimistic evaluation. This article surveys supervised learning approaches with a focus on robustness—defined as the stability of predictive performance under perturbations to data, environment, or assumptions. We organize the model space into seven families: linear and generalized linear models; tree-based models; kernel methods; instance-based methods; probabilistic generative models; neural networks; and ensemble learning. For each family we discuss inductive biases, optimization, computational complexity, calibration, and typical failure modes. We then synthesize a method-agnostic workflow spanning dataset auditing, leakage prevention, feature engineering, resampling, hyperparameter tuning, model selection, and post-hoc reliability analysis (calibration, uncertainty, and drift monitoring). Robustness strategies—regularization, data augmentation, adversarial training, cost-sensitive learning, resampling for class imbalance, monotonic constraints, conformal prediction, and causal sensitivity analysis—are reviewed with practical guidance. Case vignettes from healthcare, finance, and operations illustrate trade-offs between accuracy, interpretability, and reliability. The paper concludes with open research directions, including integrating causal structure into supervised objectives, leveraging self-supervised pretraining for tabular data, distributionally robust optimization, and aligning evaluation with societal impact.

Introduction

Supervised learning is at the heart of modern predictive systems across fields like healthcare, finance, and logistics. While algorithmic advances have improved model performance, model fragility—due to overfitting, label noise, covariate shifts, and data leakage—remains a key challenge in real-world deployment. Therefore, robustness has become a central design focus.

Key Contributions of the Paper

1. Taxonomy through Robustness Lens: Reviews major supervised learning models, focusing on their inductive biases and failure modes.

2. Auditable Modeling Workflow: Proposes a data-to-deployment pipeline to build robust, reproducible models.

3. Emerging Research Directions: Highlights advances in distributionally robust optimization (DRO), conformal prediction, and causal modeling.

Supervised Learning Model Families & Robustness

Dimensions of Robustness

• Statistical: Tolerance to outliers (e.g., Huber loss).

• Algorithmic: Stability under training changes.

• Distributional: Resilience to data drift (e.g., DRO).

• Operational: End-to-end reliability under system and pipeline variations.

Data-Centric Robustness Practices

• Data audits: Check for missingness, imbalance, leakage, and drift.

• Preprocessing: Guard against target leakage using cross-validation folds.

• Missing data handling: Use model-native features or robust imputations.

• Feature engineering: Use monotonic transforms, robust encoders (e.g., CatBoost), and quantile scaling.

• Label quality: Address noisy labels with confident learning, label smoothing, or robust loss functions.

Model Evaluation & Tuning

• Cross-validation: Use stratified/nested schemes; time-aware CV for temporal data.

• Metrics: Use task-specific metrics (e.g., PR-AUC for imbalance); report calibration scores.

• Hyperparameter tuning: Random/Bayesian search with guardrails; prefer simple models with comparable performance.

• Uncertainty: Use conformal prediction, bootstrapping, and sensitivity analysis.

• Statistical testing: Use effect size and significance tests to avoid p-hacking.

Robustness by Failure Mode

Case Studies

• Healthcare (Sepsis Prediction): Logistic regression and GBM with conformal prediction improved clinical utility.

• Finance (Credit Default): CatBoost with cost-sensitive thresholds outperformed legacy systems, preserving interpretability.

• Operations (Demand Forecasting): Time-aware GBMs with conformal intervals reduced stockouts and improved planning.

Conclusion

Robust supervised learning in data science is less about finding a universally best algorithm and more about constructing a reliable end-to-end system. By aligning inductive biases with data properties, adopting leakage-safe evaluation, and quantifying uncertainty and calibration, practitioners can substantially improve real-world performance. Emerging techniques—DRO, conformal prediction, causal regularization, and self-supervised pretraining—promise further gains in reliability. The workflow and comparative guidance presented here aim to support Scopus-ready research and industry deployments alike.

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Copyright

Copyright © 2025 Varsharani T. Dond, Mohini K. Vaidya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.