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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

2,000

Validation Set Size800
Final Training Size7,200
Data Utilization400%
How to Use This Calculator
  1. Enter the dataset size (total samples) and number of folds (k, typically 5 or 10).
  2. Input the model training time per fold in seconds.
  3. Set the performance metric (accuracy, F1, RMSE) and enter the per-fold metric values.
  4. Review the mean and standard deviation of the cross-validation score.
  5. A high standard deviation across folds indicates model instability — try regularization or more data.

How the result changes with K-Folds

K-FoldsFold SizeTraining Size per FoldTest Set Size
3.82,6327,3682,000
8.31,2058,7952,000
147149,2862,000
185569,4442,000

What each input means

Dataset Size
Total number of samples
K-Folds
Number of folds for cross-validation
Test Set Size
Percentage for test set
Validation Set Size
Percentage for validation set (from training set)

How this is calculated

Formula

Fold Size = Dataset Size / K

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    Dataset Size = 10000, K-Folds = 5, Test Set Size = 20, Validation Set Size = 10 = 4 input(s) provided
  2. Calculate Fold Size
    Fold Size
    2000 = 2000
  3. Calculate Training Size per Fold
    Training Size per Fold
    8000 = 8000
  4. Calculate Test Set Size
    Test Set Size
    2000 = 2000
  5. Calculate Validation Set Size
    Validation Set Size
    800 = 800
  6. Calculate Final Training Size
    Final Training Size
    7200 = 7200

Engine last updated . Checked against 2 independently-derived tests — how we verify calculators.

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