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AI Data Preparation & Feature Engineering › Handling Imbalanced Datasets

How does Handling Imbalanced Datasets work

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Techniques include oversampling the rare class, undersampling the common class, or using specialized algorithms and evaluation metrics designed to handle uneven class sizes.
Real-world example A fraud detection dataset with very few fraud cases compared to normal transactions uses oversampling to help the model learn to detect fraud more reliably.

Common follow-ups: What is AI Data Preparation & Feature Engineering, How is Handling Imbalanced Datasets evaluated in practice, What tools are commonly used for Handling Imbalanced Datasets

AI Data Preparation & Feature Engineering topics: Data Cleaning for AI Models Handling Missing Data Feature Scaling & Normalization
All 7 Handling Imbalanced Datasets questions