Test each row as a lone validation case. See fold metrics, residuals, and score summaries. Download polished outputs for review, sharing, compliance, and demos.
| Fold ID | Observed | Predicted | Weight | Use Case |
|---|---|---|---|---|
| 1 | 12.5 | 12.1 | 1.0 | Regression |
| 2 | 14.0 | 14.4 | 1.0 | Regression |
| 3 | Yes | No | 1.0 | Classification |
| 4 | No | No | 1.0 | Classification |
| 5 | 16.3 | 16.8 | 1.5 | Weighted regression |
The calculator uses one row per held-out observation. Each row represents one LOOCV fold.
Leave-One-Out Cross-Validation trains the model on n - 1 rows and tests on the remaining row, repeating this process for every row.
Error = Predicted − Observed
MAE = Σ|Error| / n
MSE = Σ(Error²) / n
RMSE = √MSE
MAPE = [Σ(|Error| / |Observed|) × 100] / n, excluding zero observed values
Bias = ΣError / n
R² = 1 − (Σ(Error²) / Σ(Observed − Mean Observed)²)
Accuracy = (TP + TN) / Total
Error Rate = 1 − Accuracy
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
Specificity = TN / (TN + FP)
F1 = 2 × Precision × Recall / (Precision + Recall)
MCC uses all confusion matrix cells for a balanced binary score.
Choose regression or classification mode first.
Paste one held-out prediction row per line into the data box.
For classification, set the positive class label carefully.
Click Calculate LOOCV to view summary metrics and fold details.
Review the graph, inspect the per-fold table, then export CSV or PDF if needed.
LOOCV estimates model performance by holding out one observation at a time. It is useful for small datasets because every row becomes a validation case exactly once.
Use regression mode when observed and predicted values are numeric. The calculator then returns MAE, MSE, RMSE, MAPE, bias, and R² from the held-out predictions.
Use classification mode when your target is categorical, such as Yes or No. The calculator computes accuracy, error rate, precision, recall, specificity, F1, and MCC.
Precision, recall, specificity, F1, and MCC depend on the chosen positive class. Pick the label that represents the event or outcome you most care about tracking.
Yes. Add a weight as the last value in each row. Weighted rows influence summary metrics more strongly, which can help when some observations deserve greater emphasis.
MAPE is not defined when an observed regression value is zero. The calculator skips zero observed rows for that specific metric while still computing other measures normally.
No. This tool evaluates LOOCV outcomes that you already generated elsewhere. It summarizes held-out predictions, fold errors, and validation diagnostics from your prepared results.
You can export the calculated result table as CSV and save a polished PDF report from the rendered summary area. Both export buttons appear after calculation.
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.