Skip to content
ENExplore Calculators

Train Test Split Size Calculator

Work out train test split size instantly with clear inputs, formula shown and shareable results.

About Train Test Split Size Calculator

A three-way split gives the model data to learn from, a validation set for tuning decisions, and a test set touched only once at the end. The validation share is whatever the training and test shares leave over. Row counts matter more than percentages: a 15 percent test set of 300 rows is 45 examples, far too few to separate two models with confidence.

Formula

Split sizing

train = rows x train share; test = rows x test share; validation = rows - train - test

Worked example

With the default values below, Train Test Split Size Calculator returns Training rows of 7,000. Change any input above and every figure here is recalculated in your browser.

Inputs used

Total rows in the datasetminimum 3
10,000
Training shareaccepts 1–98 %
70 %
Test shareaccepts 1–98 %
15 %

Results

Training rows
7,000
Validation rows
1,500
Test rows
1,500
Remaining share
Validation share is 15 percent

Step by step

  1. 1Values used
  2. 2Split sizing
  3. 3Training rows= 7,000
  4. 4Validation rows= 1,500
  5. 5Test rows= 1,500
  6. 6Remaining share= Validation share is 15 percent

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 whole numbers, 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

Why keep validation and test separate?

Every tuning decision made against a set leaks information into it. Holding a genuinely untouched test set is the only way to get an unbiased estimate of generalisation.

What if the dataset is small?

Prefer k-fold cross-validation for model selection and keep one modest held-out test set. Repeated fitting uses every row for training at some point.

Related Calculators

Train Test Split Size Calculator | Calcrivo