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Data Leakage Risk Check Calculator

Work out data leakage risk check instantly with clear inputs, formula shown and shareable results.

About Data Leakage Risk Check Calculator

Leakage inflates offline scores in three common ways: duplicate or near-duplicate rows straddling the split, features that could only be known after the outcome, and random splitting of time-ordered data so the model trains on the future. This check weights those three signals into one score so a suspiciously good validation result can be triaged before it reaches a stakeholder deck.

Formula

Composite risk score

score = min(60, 3 x overlap%) + min(30, 15 x post-outcome features) + 20 if chronological order is violated

Worked example

With the default values below, Data Leakage Risk Check Calculator returns Leakage risk score (0-100) of 68.0. Change any input above and every figure here is recalculated in your browser.

Inputs used

Rows appearing in both train and testminimum 0
120
Test rowsminimum 1
2,000
Features computed after the outcomeminimum 0
2
Chronological order respectedone of Yes - split by time, No - random split on time-series data
No - random split on time-series data

Results

Leakage risk score (0-100)
68.0
Test rows also seen in training
6.00%
Risk band
High - investigate before reporting
Next action
Remove post-outcome features and rebuild the split

Step by step

  1. 1Values used
  2. 2Composite risk score
  3. 3Leakage risk score (0-100)= 68.0
  4. 4Test rows also seen in training= 6.00
  5. 5Risk band= High - investigate before reporting
  6. 6Next action= Remove post-outcome features and rebuild the split

Assumptions and limits

  • The result is derived only from the 4 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

Why is a random split wrong for time series?

It lets the model learn from records that happen after the ones it is scored on, which is impossible at inference time. Always split at a cut-off date.

How do near-duplicates cause leakage?

The model effectively memorises a row and is then tested on its twin. Deduplicate on a business key and on content hashes before splitting.

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