Imbalanced Datasets
Imbalanced datasets refer to datasets where the distribution of classes is not equal. In other words, one class has significantly more samples than the other class(es). This is a common problem in many real-world scenarios such as fraud detection, disease diagnosis, and anomaly detection. Imbalanced datasets can lead to biased models that perform poorly on the minority class. Therefore, it is important to handle imbalanced datasets appropriately in order to build accurate and fair models. There are several techniques to handle imbalanced datasets such as oversampling the minority class, undersampling the majority class, and using synthetic data generation methods.
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