Why does binary cross entropy become less reliable on highly imbalanced datasets?

Why Binary Cross Entropy Becomes Less Reliable on Imbalanced Data

binary cross entropy is built to reward average error reduction, not class balance. When one class is rare, a model can improve its loss mainly by getting the common class right and still remain weak where the minority class matters most. That makes the metric feel stable while the decision boundary quietly drifts toward majority-class comfort.

The problem is not that BCE stops working mathematically, but that its learning signal becomes misaligned with the real objective. In an imbalanced dataset, many minority examples contribute too little aggregate loss to reshape training unless they are heavily misclassified. This is why practitioners often see high accuracy, decent loss curves, and poor recall on the rare class at the same time. In practice, many teams notice the failure only after the model has already been deployed against rare but high-value cases.

For security and identity-heavy workflows, this matters because rare events are often the ones that matter most. NHIMG research notes that 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, which is exactly the kind of low-frequency, high-impact pattern a naïve loss function can underweight. Ultimate Guide to NHIs — Key Research and Survey Results

How the Loss Signal Breaks in Practice

BCE computes error per example, then averages across the batch or dataset. On a highly skewed dataset, the majority class dominates that average, so the optimiser can find a low-loss solution that is not actually useful for the rare class. The model is not being “fooled” by the metric so much as being trained on a distribution where the easiest way to reduce loss is to follow the majority pattern.

That creates a few common failure modes:

• The model predicts the majority class with high confidence and still posts an attractive loss curve.
• Rare positives may be learned only when they are extremely obvious, because ordinary minority examples barely move the gradient.
• Threshold selection becomes fragile, since a small change in cutoff can swing minority recall sharply.
• Calibration can look acceptable overall while minority-class probability estimates remain poor.

Weighted BCE changes the gradient contribution so minority mistakes matter more. Focal loss goes further by reducing the relative weight of easy examples and concentrating learning on hard misclassifications. Those choices are not interchangeable: weighted BCE is often simpler when class imbalance is the main issue, while focal loss is more useful when there are many easy negatives and a small number of hard positives. NIST SP 800-53 Rev 5 Security and Privacy Controls gives broader control context for classification quality, logging, and verification, but it does not solve the optimisation problem itself.

In practice, BCE breaks down when the dataset is both skewed and operationally asymmetric, because the training objective rewards average fit long before it rewards reliable minority detection.

Common Edge Cases and Better Evaluation Signals

Tighter class balancing often improves minority sensitivity but can increase false positives, so teams need to balance recall against review burden and downstream cost. There is no universal threshold choice for this yet; the right setting depends on whether the rare class is merely inconvenient or genuinely high risk.

One common edge case is when the minority class is rare but diverse. In that situation, weighting alone may not help much if the model never sees enough representative examples. Another is label noise: if the rare class is small and inconsistently labelled, stronger weighting can amplify noise rather than signal. Current guidance suggests checking precision, recall, PR-AUC, and per-class calibration instead of relying on overall accuracy or BCE alone.

Practitioners should also be careful not to treat loss as the final arbiter of quality. A model with a worse BCE score can be operationally better if it captures the rare class more reliably. The reverse is also true: a model with a good loss can still be unsafe if it misses the cases that matter most.

Risk and Threat Considerations

When binary cross entropy is used on imbalanced security, fraud, or detection datasets, the main risk is silent minority-class failure. The model can appear healthy because the dominant class drives the average loss, while rare but consequential events remain underdetected.

Failure mechanism: The optimiser receives too little gradient pressure from minority examples, so the learned boundary shifts toward the majority class and confidence scores become misleading in the region where rare events live.

Impact: Missed alerts, weak triage, and false reassurance in monitoring or decision systems, especially where the rare class represents compromise, abuse, or other high-consequence outcomes.

Practitioner Guidance

What to prioritise: Evaluate the rare-class objective first, not the aggregate loss. If missing positives is expensive, choose metrics and training objectives that explicitly reward minority recall and hard-example learning.

Decision rule: If the dataset is heavily skewed and the minority class is operationally important, treat plain BCE as a baseline only; move to weighted BCE, focal loss, or a cost-sensitive evaluation setup before trusting model quality.

What to verify: Check whether improvements in BCE actually translate into better precision-recall behaviour for the rare class, because a lower loss can still hide worse real-world performance.

Practitioner takeaway: The core mistake is assuming average loss tracks business value. On imbalanced data, the right question is whether the model reliably finds the rare cases that matter, not whether it looks good overall.

Related resources from NHI Mgmt Group

• Why do DAST findings become less reliable in continuous delivery environments?
• How should teams monitor binary cross entropy in production?
• What do teams get wrong about binary cross entropy and model quality?
• Why do long-running AI agents become less reliable over time?