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01/08/2026 10:47 pm
TL;DR: Class imbalance can make accuracy look strong while hiding weak minority-class performance, so Openlayer argues that confusion-matrix metrics, threshold tuning, and ROC or PR curves are better ways to evaluate models in rare-event settings. For security and fraud teams, the lesson is that metric choice changes what gets shipped, monitored, and trusted.
NHIMG editorial — based on content published by Openlayer: Dealing with class imbalance, continued
By the numbers:
- Only 5.7% of organisations have full visibility into their service accounts.
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
- 71% of NHIs are not rotated within recommended time frames, increasing the risk of compromise over time.
Questions worth separating out
Q: How should teams evaluate machine learning models when classes are highly imbalanced?
A: Teams should move away from overall accuracy and evaluate the model using metrics that reflect the business cost of each error type.
Q: Why does accuracy often fail on rare-event models?
A: Accuracy fails because it counts every prediction equally, even when the minority class is the one that matters most.
Q: What do security teams get wrong about fraud scoring?
A: They often treat scoring as a black box that either approves or blocks a transaction.
Practitioner guidance
- Replace accuracy-first model acceptance criteria Set the primary evaluation metric to recall, precision, F1, or a curve-based measure that matches the cost of false negatives and false positives in the business process.
- Review the confusion matrix for every release candidate Require model review to include true positives, false positives, true negatives, and false negatives by class.
- Tune and document operating thresholds Test multiple probability thresholds against real investigation capacity, alert fatigue, and loss tolerance.
What's in the full article
Openlayer's full blog post covers the operational detail this post intentionally leaves for the source:
- Worked examples showing how confusion matrices change under different class distributions
- Step-by-step comparison of ROC and precision-recall curves on unbalanced datasets
- Practical threshold-tuning guidance for classification models used in rare-event detection
- Code-oriented explanation of metric trade-offs for model selection and error analysis
👉 Read Openlayer's analysis of model evaluation under class imbalance →
Class imbalance in model evaluation: are your metrics keeping up?
Explore further
01/08/2026 10:56 pm
Imbalanced evaluation creates a governance blind spot: the model can be technically correct on the dominant class while operationally wrong on the rare class. That is the same failure pattern identity and security teams face when their controls are tuned to average behaviour rather than exceptional events. In a programme where false negatives matter, the metric becomes part of the control surface, not just the scorecard.
A question worth separating out:
Q: How do teams decide between ROC and precision-recall curves?
A: ROC curves are useful for comparing overall discrimination, but precision-recall curves are usually more informative when the positive class is rare. If false alarms are expensive and positives are scarce, precision-recall analysis gives a more honest view of operational performance.
👉 Read our full editorial: Class imbalance in ML model evaluation: why accuracy misleads
