Assessing and Comparing Classifier Performance with ROC Curves in R for Economics

This article shows how to use ROC curves and AUC in R to rigorously evaluate and compare classifiers for economic decision-making, selecting models and thresholds that balance predictive power with the real costs of false positives and false negatives.

Article Outline:

1. Introduction – Why rigorous classifier evaluation matters in applied economics (credit risk, default prediction, fraud screening, policy targeting) and why accuracy alone can mislead.

2. ROC Curve Fundamentals – Definitions of TPR (sensitivity/recall) and FPR (1–specificity); how thresholds trace the ROC; reading the diagonal baseline vs. the ideal top-left.

3. AUC (Area Under the ROC Curve) – Interpreting AUC as ranking quality; when AUC is more informative than accuracy or F1 in economic decision contexts.

4. Economic Decision Trade-offs – Mapping false positives/negatives to costs in lending and policy; connecting ROC positions to expected utility and risk appetite.

5. R Environment Setup – Packages used: tidyverse for data wrangling/plots, pROC/yardstick for ROC & AUC, caret for modeling workflows, plus ranger (Random Forest) and e1071 (SVM).

6. Data Preparation for an Economic Problem – Constructing features that mirror borrower characteristics; train/test split; class balance checks; avoiding leakage.

7. Training Multiple R Classifiers – Logistic regression for interpretability, Random Forest for nonlinearity, and SVM for robust margins; extracting calibrated probabilities for ROC analysis.

8. Building and Plotting ROC Curves – Computing ROC points and AUC for each model; overlaying multi-model ROC curves; adding CIs and a reference diagonal.

9. Comparing Models with AUC and Statistical Tests – DeLong test for AUC differences; partial AUC in high-specificity regions relevant to credit approvals; dominance vs. crossings.

10. Choosing Operating Thresholds – Youden’s J, cost-sensitive thresholds, and converting a chosen threshold to a confusion matrix aligned with economic objectives.

11. Robust Evaluation Practices – Cross-validation and averaging ROC/AUC; handling class imbalance (contrast with Precision-Recall); setting seeds and documenting assumptions.

12. End-to-End Example in R – Full script: data prep → model training → probability predictions → ROC/AUC computation → multi-model plot → threshold selection and interpretation.

13. Conclusion & Next Steps – Incorporating ROC analysis into credit risk pipelines, reporting standards, and extensions to calibration curves and cost curves.

Introduction

In economics, classification models are frequently applied to tasks where the stakes are high and decision-making must be informed by careful consideration of errors. For example, banks employ models to classify borrowers into categories such as likely to repay or likely to default. Governments apply classifiers in detecting welfare fraud or targeting subsidies. In such contexts, the implications of misclassification extend beyond model accuracy, affecting financial stability, resource allocation, and social equity. Thus, evaluation methods must capture the nuances of classifier performance.

Receiver Operating Characteristic (ROC) curves provide a comprehensive framework for assessing classifiers. By plotting sensitivity against the false positive rate across thresholds, ROC curves reveal how a classifier performs under varying decision criteria. This article explores ROC curves and Area Under the Curve (AUC) analysis in depth, contextualizing them within economics and demonstrating their use with a reproducible example in R. We simulate an economic dataset that mirrors credit default prediction, train multiple classifiers, and evaluate them using ROC and AUC. The goal is to empower economists and data scientists to apply these techniques in their own research and practice.

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