# Oversampling

> Source: https://sukruyusufkaya.com/en/glossary/oversampling
> Updated: 2026-05-13T20:01:42.112Z
> Type: glossary
> Category: veri-bilimi-ve-veri-yonetimi
**TLDR:** An approach that increases the number of minority-class examples to make them more visible in the dataset.

<p>Oversampling aims to improve the model’s ability to learn the minority class by increasing the number of examples belonging to it. In its simplest form, this is done by reusing existing examples; in more advanced forms, synthetic examples may be generated. This approach can reduce the model’s tendency to favor the majority class. However, if applied carelessly, it can increase overfitting risk and create a false sense of balance, especially in small datasets. Oversampling is a powerful tool, but one that must be used thoughtfully.</p>