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%
Fold Size
2,000
Training Size per Fold
8,000
Test Set Size
200,000
How to Use This Calculator
- Start by filling in the input fields below. Results update instantly as you type, so you can experiment with different values to see how they affect the outcome.
- Dataset Size — Total number of samples Accepts values from 10 to 10,000,000 (default: 10,000).
- K-Folds — Number of folds for cross-validation Accepts values from 2 to 20 (default: 5).
- Test Set Size — Percentage for test set default: 20.
- Validation Set Size — Percentage for validation set (from training set) default: 10.
- Once all inputs are set, review your results in the Results panel. Here's what each output means:
- Fold Size — shown as a numeric value. This is the primary result of this calculator.
- Training Size per Fold — shown as a numeric value. This is the primary result of this calculator.
- Test Set Size — shown as a numeric value. This is the primary result of this calculator.
- Validation Set Size — shown as a numeric value.
- Final Training Size — shown as a numeric value.
- Data Utilization — shown as a percentage.
- Explore the related calculators below if you need deeper analysis or want to approach this topic from a different angle.
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Formula
Fold Size = Dataset Size / KRelated Calculators
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