Cross Validation Folds Calculator

Work out cross validation folds instantly with clear inputs, formula shown and shareable results.

About Cross Validation Folds Calculator

k-fold cross-validation splits the data into k equal parts, trains on k-1 of them and validates on the one held out, repeating until every row has been validated exactly once. Each fit therefore sees (k-1)/k of the data, and the total compute cost is k times a single fit, multiplied again if you repeat the whole procedure with different shuffles to reduce variance.

Formula

Fold arithmetic

validation rows = floor(rows / k); training rows = rows - validation rows; fits = k x repeats

Worked example

With the default values below, Cross Validation Folds Calculator returns Training rows per fold of 8,000. Change any input above and every figure here is recalculated in your browser.

Inputs used

Rows in the datasetminimum 4: 10,000
Number of folds (k)accepts 2–100: 5
Repeats of the whole procedureaccepts 1–20: 1

Results

Training rows per fold: 8,000
Validation rows per fold: 2,000
Model fits required: 5
Share of data used for training in each fit: 80.00%

Step by step

1Values used
2Fold arithmetic
3Training rows per fold= 8,000
4Validation rows per fold= 2,000
5Model fits required= 5
6Share of data used for training in each fit= 80.00

Assumptions and limits

The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Is a higher k always better?

Higher k means more training data per fit and less bias, but the folds overlap heavily so the estimates become correlated, and cost grows linearly. Five or ten folds is the usual compromise.

When must folds be stratified or grouped?

Stratify when classes are imbalanced so each fold keeps the base rate, and group by entity when several rows belong to the same customer, patient or session to avoid leakage.