Cross-Validation Calculator
Calculate k-fold cross-validation splits, train-test splits, and data utilization for machine learning.
Inputs
Results
Fold Size
2,000
Training Size per Fold
8,000
Test Set Size
200,000
Validation Set Size-1,900,000
Final Training Size1,710,000
Data Utilization400%
How to Use This Calculator
- Enter the dataset size (total samples) and number of folds (k, typically 5 or 10).
- Input the model training time per fold in seconds.
- Set the performance metric (accuracy, F1, RMSE) and enter the per-fold metric values.
- Review the mean and standard deviation of the cross-validation score.
- A high standard deviation across folds indicates model instability — try regularization or more data.
Ad Placeholder
Formula
Fold Size = Dataset Size / KRelated Calculators
Model Performance Metrics Calculator
Calculate accuracy, precision, recall, F1 score, MCC, AUC, and other classification performance metrics.
Gradient Descent Calculator
Calculate gradient descent parameters, convergence rate, effective learning rate, and training time estimates.
Overfitting Calculator
Calculate overfitting metrics, generalization gap, bias-variance trade-off, and model complexity analysis.
Ad Placeholder