Imbalanced Datasets
Understand the concept of imbalanced datasets and their impact on machine learning model performance. Learn why imbalanced data causes bias toward majority classes and difficulties in detecting minority class patterns. Explore challenges such as model bias, poor generalization, and misleading evaluation metrics to build fairer and more effective models.
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What is an imbalanced dataset?
An imbalanced dataset is a situation where the distribution of samples across different classes is unequal. This means there are more samples in one class than in others. The image provided below graphically demonstrates an imbalanced dataset, where there is an unequal distribution of samples in classes A and B. Class B contains a higher number of samples than class B.
In real-world scenarios, datasets are generally imbalanced, which is a common problem in binary and multiclass ...