Imbalance is common wherever the outcome of interest is rare: churn, bankruptcy, defects and fraud. Libraries such as imbalanced-learn collect standard techniques for training on this kind of data.
CUBIG’s UCI Iranian Churn experiment (October 2026) shows the scale. The evaluation set held 99 churners among 630 customers. With the model settings unchanged, a logistic regression found 42 of them before data preparation and 78 afterward, while false alarms rose from 8 to 41.
Common ways teams handle imbalance:
- resampling, by adding minority examples or removing majority examples in the training data
- class weights that make errors on the rare class cost more during training
- choosing the decision threshold for the task instead of keeping the default
- collecting or preparing more examples of the rare case
Why is accuracy misleading on an imbalanced dataset?
- If 2% of cases are positive, a model that predicts negative every time is 98% accurate and finds nothing.
- Precision and recall and the F1 score look at the rare class directly.
- The confusion matrix shows how many rare cases were found and how many false alarms came with them.
Where does an imbalanced dataset break in AI work?
Every fix changes the mix of errors. Finding more rare cases usually brings more false alarms, as the comparisons in CUBIG’s summary of five data preparation experiments show. Evaluate on records that keep the real class ratio, never on resampled evaluation data, and judge the result by what a missed case and a false alarm each cost in the task.