F1 is more honest because it collapses toward zero when either precision or recall breaks down. Accuracy can stay high by predicting the majority class repeatedly, even if the model finds nothing useful. F1 prevents that failure mode by focusing on true positives, false positives, and false negatives, which are the counts that actually measure positive-class quality.
Why F1 Is the Better Reality Check When Classes Are Imbalanced
accuracy is easy to inflate in skewed datasets because it counts every prediction equally, even when the model mostly guesses the dominant class. F1 is more useful when the positive class is the point of the model, because it forces precision and recall to stay in view together. That makes it harder to hide a system that looks efficient on paper but fails on the cases that matter.
For practitioners, the key issue is not whether the model is “right” most of the time, but whether it is right for the minority outcome that actually drives action. A classifier that misses rare positives can still post a flattering accuracy score while delivering little operational value. F1 reduces that blind spot by making false negatives and false positives visible in a single score.
That distinction matters in security-adjacent analytics too. If a detector is meant to catch a scarce but important event, the metric should reward useful recovery of that event rather than majority-class comfort. NHI Mgmt Group research shows how often organisations already struggle with low visibility into service accounts and secrets, which is exactly the kind of environment where inflated “overall” metrics can be misleading. Ultimate Guide to NHIs — Key Research and Survey Results
In practice, teams usually discover the gap only after a rare-class miss has already slipped through production reporting.
How F1 Changes the Way You Judge a Model
F1 is the harmonic mean of precision and recall, which means it drops sharply when either side is weak. That is why it behaves more honestly than accuracy in skewed settings: it will not let a model “coast” on correct negative predictions while failing to detect the rare positive events. If the dataset is 99 percent negative, a trivial model can appear excellent on accuracy and still be useless for the task.
In practical terms, F1 asks a more operational question: when the model predicts a positive, how often is it right, and when a positive exists, how often does it find it? Those are the counts that matter when the positive class is the event you are trying to surface, triage, or block. Accuracy does not distinguish between a model that finds the important cases and a model that ignores them, as long as the majority class remains dominant.
A useful way to interpret the metric is to compare it with the decision cost:
- High precision with low recall means the model is cautious but misses too much.
- High recall with low precision means the model finds most positives but creates too many false alarms.
- F1 forces both sides to contribute, so it is harder to overstate performance with one strong dimension and one weak one.
For readers who want the metric definition in the broader measurement context, the NIST SP 800-53 Rev 5 Security and Privacy Controls catalogue is useful as a control-reference point for how measurement, monitoring, and accountability are treated in mature programmes. The reporting challenge is not unique to machine learning: any control can look healthy if the metric ignores the failure mode it was meant to catch.
It breaks down when the positive class is not actually the outcome of interest, because then F1 over-weights a minority event that may not deserve operational priority.
When Accuracy Still Helps and F1 Needs Context
Stricter evaluation often increases interpretive burden, so teams have to balance a cleaner signal against the cost of explaining it to stakeholders. Accuracy is still useful when classes are roughly balanced, when every error has similar cost, or when the objective is simple overall correctness rather than positive-class performance. In those cases, F1 can be less intuitive than necessary.
There is also no universal standard for using F1 alone. Best practice is evolving toward metric sets that match the decision context, because F1 does not tell you about true negatives, probability calibration, or the cost asymmetry between different error types. A model can post a strong f1 score and still be poorly calibrated or unsuitable for threshold-based operations.
The main trade-off is that F1 is intentionally selective: it is better at exposing weak minority-class behaviour, but it is not a full performance summary. Practitioners should treat it as the right headline metric when rare positives matter, then pair it with threshold analysis, confusion-matrix review, or cost-based measures where the deployment decision depends on more than one kind of error. In other words, F1 is more honest about minority-class quality, but honesty about one dimension is not the same as completeness across all dimensions.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0, CIS Controls v8 and NIST AI RMF set the technical controls, while ISO/IEC 42001:2023 define the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | DE.CM — Security Continuous Monitoring | Skewed metrics can hide monitoring failure and blind spots in control performance. |
| Recommendation — Measure minority-class misses with monitoring metrics that expose detection gaps. | ||
| CIS Controls v8 | 8 — Audit Log Management | Reliable evaluation depends on counts that reflect actual events, not just overall correctness. |
| Recommendation — Track event-quality measures that reveal false negatives and false positives in detection. | ||
| NIST AI RMF | MAP 3.1 — Measure and Monitor AI Risks | Model evaluation should use metrics that reflect the actual risk and performance objective. |
| Recommendation — Choose evaluation measures that align with the model’s intended risk and impact. | ||
| ISO/IEC 42001:2023 | 6.1 — Actions to Address Risks and Opportunities | Metric choice should be tied to the risk posture of the AI use case, not generic accuracy. |
| Recommendation — Link model metrics to the specific risk the system is expected to manage. | ||
Practitioner Guidance
What to prioritise: Use F1 as the primary score when the business value sits in finding the minority class, not in maximising aggregate correctness. If the model is meant to surface rare events, accuracy should be treated as a supporting statistic, not the deciding one.
What to verify: Check whether the positive class is genuinely the operational target and whether false negatives or false positives are the more expensive error. If neither error dominates, F1 may be helpful but should not be the only metric guiding deployment.
Common mistake: Do not accept a high accuracy figure as evidence of a good classifier when class imbalance is severe. That usually means the model has learned the majority baseline, not the problem.
Practitioner takeaway: The metric should reflect the decision you care about, and in skewed datasets that usually means proving the model can recover the rare class rather than merely repeat the common one.
