Calculator Inputs
Use summary counts for overall coverage, then optionally add per-fold or per-batch rows as Label, Total, Covered.
Example Data Table
This example shows four evaluation folds for a prediction interval or conformal set workflow.
| Fold | Total Predictions | Covered Predictions | Coverage % | Comment |
|---|---|---|---|---|
| Fold 1 | 120 | 111 | 92.50% | Near target, slightly conservative miss count. |
| Fold 2 | 140 | 130 | 92.86% | Stable performance with modest under-coverage. |
| Fold 3 | 110 | 101 | 91.82% | Lowest fold coverage in the sample. |
| Fold 4 | 130 | 121 | 93.08% | Best fold in this small evaluation set. |
| Total | 500 | 463 | 92.60% | Use these totals as the default calculator example. |
Formula Used
Coverage = Covered Predictions / Total Predictions
Miss Rate = 1 - Coverage
Coverage Gap = Empirical Coverage - Target Coverage
Center = (p + z² / 2n) / (1 + z² / n)Margin = z × sqrt((p(1-p)/n) + z²/(4n²)) / (1 + z²/n)
In machine learning, coverage usually means the true label or value is contained in a predicted set, interval, or uncertainty region. High coverage improves reliability, but very wide intervals or large label sets may reduce usefulness.
How to Use This Calculator
FAQs
1) What does coverage probability mean in AI and machine learning?
It measures how often the true outcome falls inside the model’s predicted set, interval, or uncertainty region. It is common in conformal prediction, probabilistic forecasting, and uncertainty-aware classification.
2) Is higher coverage always better?
Not always. A model can reach high coverage by producing overly wide intervals or large label sets. Good systems balance coverage with efficiency, sharpness, and practical decision usefulness.
3) Why does this calculator show a Wilson interval?
Empirical coverage from finite samples has uncertainty. The Wilson interval is a stable binomial confidence interval that often performs better than a simple normal approximation, especially with smaller samples.
4) What is the difference between target coverage and empirical coverage?
Target coverage is the desired reliability level, such as 95%. Empirical coverage is what your evaluation data actually achieved. The difference between them is the coverage gap.
5) When is under-coverage dangerous?
Under-coverage means the model misses true outcomes more often than intended. This can be risky in medical AI, finance, forecasting, and other systems where uncertainty calibration matters.
6) What counts as a covered prediction?
A prediction is covered when the true label or observed value lies inside the predicted label set, interval, or uncertainty range for that sample.
7) Why can batch-level coverage help?
Batch results reveal instability across folds, time periods, domains, or segments. Strong overall coverage can still hide weak reliability in specific slices of data.
8) Can I use this for conformal prediction evaluation?
Yes. It is suitable for conformal intervals, conformal sets, calibrated risk control summaries, and other workflows where coverage is estimated from covered versus total predictions.