Class Imbalance Ratio Calculator
Calculate the imbalance ratio between majority and minority classes and see if it exceeds common concern thresholds.
Inputs
Imbalance Ratio
19.00:1
Minority Class Share
5.00%
Concern Level
Severe — resampling/weighting strongly recommended
Step by step
Imbalance ratio
9500 ÷ 500
= 19.00 : 1
Minority class share of total
500 ÷ (9500 + 500)
= 5.00%
How it works
The class imbalance ratio divides the majority class count by the minority class count, giving a single number that summarizes how skewed a classification dataset is. A ratio below 3:1 is generally considered mild and often trainable without special handling, while ratios above 10:1 typically require resampling, class weighting, or specialized loss functions (e.g. focal loss) to prevent the model from simply predicting the majority class.
Formula
imbalance_ratio = majority_class_count / minority_class_count
- majority_class_count
- Number of samples in the majority class
- minority_class_count
- Number of samples in the minority class
Frequently Asked Questions
At what ratio should I start worrying?
Most practitioners start applying mitigation techniques around a 10:1 ratio, though the right threshold depends on how costly minority-class errors are for your application (e.g. fraud or disease detection tolerate less imbalance).
Does accuracy still make sense at high imbalance?
No — at high imbalance, a model predicting only the majority class can achieve high accuracy while being useless; use precision, recall, F1, or AUPRC instead.
What can I do about severe imbalance?
Common approaches include oversampling the minority class (SMOTE), undersampling the majority class, class-weighted loss functions, or reframing the problem as anomaly detection.