Classification
Machine Learning
A situation where classes in a dataset are not represented equally potentially biasing model performance.
Detailed Explanation
Class imbalance occurs when one class significantly outnumbers others in a dataset, leading to biased model training. This imbalance can cause models to favor the majority class, reducing the accuracy for minority classes. Addressing class imbalance is crucial through techniques like resampling, synthetic data generation, or appropriate evaluation metrics to ensure fair and effective model performance across all classes.
Use Cases
- •Detecting fraud in banking transactions by balancing legitimate and fraudulent case datasets to improve model accuracy and fairness.
Related Terms
Other terms in the Machine Learning category