Beginner · Data
Class-imbalanced dataset
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TL;DR. A dataset where the number of examples for each class is significantly different, potentially biasing model performance.
Technical Definition
A dataset where the number of examples for each class is significantly different, potentially biasing model performance.
How it works
A class-imbalanced dataset features a substantial disparity in the number of instances across its various classes. For example, a dataset with a million negative labels and only ten positive labels is highly imbalanced, making it difficult for a model to learn the characteristics of the minority class. This contrasts with class-balanced datasets, where class distribution is more even.
Related Concepts
- Dataset — An organized collection of examples used to train, validate, or test a model.
- Class-balanced dataset — A dataset where each category has roughly an equal number of examples, ensuring fair representation for all classes.