Resampling Requirement Calculator

Work out resampling requirement instantly with clear inputs, formula shown and shareable results.

About Resampling Requirement Calculator

To reach a target minority share t, the minority class must hold t/(1-t) times as many rows as the majority class. Oversampling adds that shortfall; undersampling instead deletes majority rows until the same ratio holds. Oversampling keeps all information but risks overfitting duplicated points, while undersampling throws away real data — the choice depends on how much majority data you can afford to lose.

Formula

Balance targets

required minority = t x majority / (1 - t); rows to add = required - current; majority to keep = minority x (1 - t) / t

Worked example

With the default values below, Resampling Requirement Calculator returns Minority rows to synthesise or duplicate of 5,834. Change any input above and every figure here is recalculated in your browser.

Inputs used

Majority class rowsminimum 1: 9,500
Minority class rowsminimum 1: 500
Target minority shareaccepts 1–50 %: 40 %

Results

Minority rows to synthesise or duplicate: 5,834
Majority rows to drop instead: 8,750
Current minority share: 5.000%
Majority : minority ratio: 19.00

Step by step

1Values used
2Balance targets
3Minority rows to synthesise or duplicate= 5,834
4Majority rows to drop instead= 8,750
5Current minority share= 5.000
6Majority : minority ratio= 19.00

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 3 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

Should I aim for a perfect 50/50 balance?

Rarely. Full balancing distorts the base rate so predicted probabilities need recalibration. A 20 to 40 percent minority share usually captures most of the benefit.

When is class weighting better than resampling?

When the model supports it. Class weights change the loss without duplicating or discarding rows, so training stays fast and probabilities stay closer to calibrated.