Machine learning evaluation addresses a critical gap: a model that achieves high accuracy on held-out test data may fail in production when applied to different populations, time periods, or use cases. Proper evaluation requires understanding not just accuracy metrics but also false-positive/negative tradeoffs, generalization across domains, temporal stability, and alignment with the downstream task's objectives.
Key challenges
- Metric mismatch: A high ROC-AUC (diagnostic ability) doesn't guarantee high precision and recall in imbalanced populations
- Distribution shift: Training and test data may not reflect the distribution where the model will be deployed
- Temporal drift: Model performance degrades as patterns change over time
- Cross-domain generalization: Features learned on one population may not transfer to others
- Task misalignment: Optimizing the wrong metric can produce models that perform well statistically but fail in practice
Key papers
- 2020-rauchfleisch-bot-detection-false-positives — Critical evaluation of Botometer demonstrating that high diagnostic ability (ROC-AUC 0.85) masks poor practical performance (false-positive rate 41–76%) due to class imbalance; argues that researchers must evaluate classifiers using precision-recall curves and validate on labeled data from their actual use case rather than relying on training-set metrics
Connections
- bot-detection — practical evaluation of bot detection systems
- false-positives — key evaluation metric for imbalanced classification
- computational-social-science — methodological rigor in empirical social science research