Train/Test Split

About this tool

Use this browser-local CSV splitter to create reproducible train, test and optional validation sets for machine-learning experiments. Paste a CSV, choose a test size as either a fraction (0.2) or an absolute row count (50), and the tool returns labelled CSV sections, one selected split, a summary table, or JSON containing every split.

The default is an 80/20 train/test split with seed = 42, so the same CSV and settings always produce the same rows. Rows keep their original order inside each output split, which makes the result easier to inspect and diff.

You can stratify on a label column to keep class balance across splits, quantile-bin a numeric stratification column, group related rows so they never leak across train and test, or turn shuffling off for a sequential time-series split where the newest rows land in the test set.

Worked example

Input:

id,label
1,a
2,b
3,a
4,b
5,a
6,b
7,a
8,b
9,a
10,b

With test_size = 0.2, validation_size = 0, shuffle = false, and output = sections, the rows are split sequentially:

# train (8 rows)
id,label
1,a
2,b
3,a
4,b
5,a
6,b
7,a
8,b

# test (2 rows)
id,label
9,a
10,b

For a stratified split, keep shuffle on and set stratify_column = label. For a leakage-safe grouped split, leave stratify_column empty and set group_column to a patient, user, document, or other group identifier.

Limits and edge cases

• test_size and validation_size below 1 are fractions of the data rows; values 1 or above are rounded to row counts.
• The split must leave at least one training row. If test plus validation consumes every row, the tool returns an error.
• stratify_column and group_column are mutually exclusive. Stratification balances classes; grouping prevents leakage, and combining both would imply a more complex grouped-stratified algorithm.
• Stratification requires shuffle = true. Turn shuffle off only for a sequential or time-series split.
• Grouped splits keep each group intact, so row counts can land near the requested sizes rather than exactly on them when groups are large.
• CSV input is parsed in memory. Convert Excel or Parquet to CSV first, and use a local script for very large datasets that do not fit comfortably in the browser.

FAQ