Undersampling Ratio Calculator
Calculate the reduced majority-class size after undersampling to reach a target class ratio, keeping the minority class unchanged.
Inputs
e.g. 1 = fully balanced (1:1); 2 = majority is 2x minority after resampling.
New Majority Class Size
1,000
Samples Removed
8,500
Total Data Retained
15.0%
Step by step
Current ratio
9500 ÷ 500
= 19.00 : 1
New majority class size
500 × 2
= 1000
Samples removed
9500 − 1000
= 8500
How it works
Undersampling reduces the majority class to a target size while keeping the minority class untouched, shrinking the overall dataset but balancing the class ratio. This calculator computes the new majority class size as minority count × target ratio, and reports how many majority-class samples must be removed and what fraction of the total dataset remains — a key tradeoff since undersampling discards potentially useful data.
Formulas
New majority size
new_majority = round(minority_count × target_ratio)
- minority_count
- Number of minority-class samples
- target_ratio
- Desired majority:minority ratio
Samples removed
samples_removed = current_majority - new_majority
- current_majority
- Current majority class size
- new_majority
- Target majority class size
Frequently Asked Questions
When is undersampling preferable to oversampling?
Undersampling is attractive when you have an abundance of majority-class data and want faster training with no synthetic data, but it risks discarding informative majority-class examples.
How much data loss is too much?
If undersampling would discard more than ~50-70% of your majority class, consider combining it with some oversampling of the minority class instead of undersampling alone.
Which majority samples should be removed?
Random undersampling removes samples uniformly at random; more advanced methods (e.g. Tomek links, NearMiss) selectively remove majority samples near the decision boundary to preserve information.
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