Class imbalance in ML model evaluation: why accuracy misleads

At a glance

What this is: This is an analysis of why standard ML evaluation metrics can mislead when classes are highly imbalanced, and why confusion-matrix-based measures and threshold curves provide a truer view of model performance.

Why it matters: It matters because identity, fraud, and security programmes increasingly depend on classifiers that must catch rare events without over-accepting false confidence, which makes metric selection a governance decision, not just a modelling choice.

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.
• 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.

👉 Read Openlayer's analysis of model evaluation under class imbalance

Context

Class imbalance is a common machine learning problem when one class appears far more often than another, and it becomes especially dangerous when teams use accuracy as the main success metric. In rare-event settings, a model can appear strong while failing exactly where it matters most, which is why metric choice is a governance issue for fraud, identity verification, and security workflows.

For security and identity programmes, the analogy is familiar: rare but high-impact events are easy to miss if evaluation rewards the majority case. That is why the article’s focus on recall, precision, F1, ROC, and precision-recall curves is directly relevant to any programme that depends on detection, triage, or risk scoring under heavy class imbalance.

Key questions

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. Recall, precision, F1, confusion matrices, and threshold-based curve analysis are better choices because they expose minority-class performance instead of averaging it away.

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. A model can predict the majority class almost all the time and still look strong, while missing the rare positives that the business actually needs to detect.

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. In practice, the score needs to be explainable, tunable, and tied to business context so teams can understand false positives, support appeals, and adjust thresholds as fraud patterns shift.

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.

Technical breakdown

Why accuracy breaks on imbalanced datasets

Accuracy treats every prediction as equally valuable, which is fine when classes are roughly balanced but misleading when one class dominates. In a confusion matrix, true positives, true negatives, false positives, and false negatives expose different error types, yet accuracy collapses them into a single ratio. A classifier that predicts the majority class almost all the time can still score highly while missing most minority-class cases. In rare-event problems, that means the metric optimises the wrong behaviour. For security and identity use cases, this is the difference between a model that looks disciplined in reporting and one that actually detects the events you care about.

Practical implication: choose class-sensitive metrics before model selection starts, not after a misleading baseline has already been accepted.

Recall, precision, and F1 as evaluation controls

Recall measures how many actual positives the model finds, precision measures how many predicted positives are correct, and F1 balances the two. These metrics let practitioners focus on the slice of performance that matters most instead of averaging away minority-class failure. If false negatives are expensive, recall becomes the priority. If false positives create unacceptable noise or cost, precision matters more. F1 is useful when teams need a balance, but it still hides some trade-offs, so it should be treated as a decision aid rather than a final verdict. The core idea is that evaluation must reflect business risk, not just mathematical convenience.

Practical implication: map the metric to the operational failure mode, then document why that metric is the right one for the use case.

ROC and precision-recall curves under threshold tuning

Many classifiers output probabilities, which lets teams choose a threshold instead of taking the default 0.5 cutoff. Lower thresholds increase sensitivity and false positives, while higher thresholds reduce false positives at the cost of more missed positives. ROC curves show the trade-off between false positive rate and true positive rate, and precision-recall curves are often more informative when the positive class is rare. AUC compresses the ROC view into a single score, which is useful for comparison but not for understanding operating point choice. The key lesson is that threshold selection is part of model governance, not a tuning afterthought.

Practical implication: test multiple thresholds against real operating costs and lock the chosen threshold to a documented deployment policy.

NHI Mgmt Group analysis

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.

Rare-event detection should be judged by decision cost, not aggregate accuracy: the article’s threshold discussion maps directly to risk-based evaluation. When the cost of missing a positive is materially higher than the cost of investigating a false alarm, recall and precision trade-offs should drive model acceptance. In practice, that means teams need explicit loss assumptions before model approval.

Confusion-matrix thinking belongs in operational review, not just data science notebooks: separating true positives, false positives, true negatives, and false negatives creates a language that security, fraud, and identity teams can share. Minority-class blindness: the specific failure mode here is treating rare outcomes as statistically unimportant until the model is already in production. Practitioners should use that concept when reviewing detection or risk-scoring systems.

Threshold governance is the real control, not the model alone: a classifier that outputs probabilities still needs an operating point, and that choice determines whether the system behaves conservatively or aggressively. For identity and fraud workflows, the right threshold is the one aligned to downstream investigation capacity and harm tolerance. Teams should govern the threshold as part of the release process.

What this signals

Rare-event modelling is structurally similar to identity and security detection problems, where the events you most need to catch are also the least common. For programmes that monitor service accounts, API keys, or anomalous access, the evaluation question is not whether the model performs well on average but whether it preserves enough sensitivity at the operating point that matters.

Minority-class blindness: this is the practical risk when teams optimise for the easiest metric instead of the most consequential error. That same pattern appears in access governance, fraud screening, and alert triage, where a low false-alarm rate can hide a dangerous miss rate. Practitioners should treat metric selection as part of the control design, not as a reporting preference.

For practitioners

• 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. Do not approve a model because the headline accuracy looks strong on an imbalanced test set.
• Review the confusion matrix for every release candidate Require model review to include true positives, false positives, true negatives, and false negatives by class. This makes minority-class failure visible before deployment and prevents aggregate scoring from hiding the risk.
• Tune and document operating thresholds Test multiple probability thresholds against real investigation capacity, alert fatigue, and loss tolerance. Record the chosen threshold as an explicit deployment control so future changes are auditable.
• Compare ROC and precision-recall curves where imbalance is extreme Use ROC for broad discrimination testing, but rely on precision-recall curves when the positive class is rare and the number of false alarms matters. This gives a clearer picture of model behaviour at the operating point that will actually be used.

Key takeaways

• Accuracy can be misleading in imbalanced ML problems because it hides weak minority-class performance behind a strong majority-class score.
• Recall, precision, F1, and threshold curves give a more honest view of rare-event models because they expose the trade-offs that matter operationally.
• Threshold selection should be governed like any other production control, with documented costs, capacity, and risk tolerance behind the chosen operating point.

Key terms

• Class Imbalance: Class imbalance occurs when one category in a dataset appears far more often than another, making aggregate accuracy misleading. In practice, models can score well by learning the dominant class while missing the rare class that often matters most in fraud, identity, or risk detection.
• Confusion Matrix: A confusion matrix is a table that separates model predictions into true positives, true negatives, false positives, and false negatives. It gives practitioners a clearer view of error types than a single score and is the starting point for choosing metrics that match operational risk.
• Precision-Recall Curve: A precision-recall curve shows how precision and recall change as the model threshold changes. It is especially useful when positive cases are rare, because it highlights how many detected positives are actually correct and how many real positives the model fails to catch.
• Threshold Tuning: Threshold tuning is the process of selecting the probability cutoff that turns a model score into a decision. It is a governance choice because the threshold controls the balance between false positives and false negatives and therefore shapes operational cost and risk.

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

👉 Openlayer's full post covers the metric trade-offs, threshold choices, and curve analysis in more depth.

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It helps practitioners connect identity controls to the broader security decisions that shape detection and access risk.