Measure uncertainty across validation folds with confidence settings. Review mean score, spread, and bounds instantly. Built for practical model evaluation and reliable reporting workflows.
| Fold | Validation Score | Difference From Mean | Squared Difference |
|---|---|---|---|
| 1 | 0.82 | 0.00 | 0.0000 |
| 2 | 0.79 | -0.03 | 0.0009 |
| 3 | 0.85 | 0.03 | 0.0009 |
| 4 | 0.81 | -0.01 | 0.0001 |
| 5 | 0.84 | 0.02 | 0.0004 |
For this dataset, the mean score is 0.8220. A 95% t interval gives an approximate confidence interval of 0.7936 to 0.8504.
Mean fold score: x̄ = (Σxi) / n
Sample standard deviation: s = √(Σ(xi − x̄)² / (n − 1))
Standard error: SE = s / √n
Margin of error: ME = critical value × SE
Confidence interval: x̄ ± ME
Use the t interval when the fold count is limited and sample variability matters. Use the z interval when you prefer a normal approximation.
It estimates a confidence interval around the average cross-validation score. This helps you judge how stable model performance looks across validation folds instead of trusting only one summary number.
Use a t interval when fold counts are modest and variability is estimated from the fold sample itself. This is often the safer choice for common k-fold evaluation workflows.
A z interval is acceptable when you intentionally use a normal approximation, especially with many folds or when you want a simpler estimate. It is usually less conservative than a t interval.
Yes. Enter fold values in decimal form and switch output to percent, or enter percent-like values consistently as raw numbers. Consistency matters more than the display format.
No. It summarizes uncertainty in observed cross-validation folds. Real-world deployment may differ because of drift, leakage, sampling bias, or changing production conditions.
Benchmark comparison shows whether your mean score is above or below a target and whether that target sits inside the estimated interval. This helps with practical model selection decisions.
Large variation widens the interval through a bigger standard error. That usually signals unstable model behavior, limited data, inconsistent preprocessing, or an over-sensitive training setup.
Yes. Accuracy, F1, AUC, precision, recall, RMSE, and similar metrics can be analyzed, provided each fold produces a comparable numeric result.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.