Imbalanced Data

Imbalanced data refers to a situation where the distribution of classes in the target variable is not equal. In other words, one class has significantly more or fewer instances 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 data can lead to biased models that perform poorly on the minority class. Therefore, it is important to handle imbalanced data appropriately to ensure that the model can learn from both classes equally. There are several techniques to handle imbalanced data such as oversampling the minority class, undersampling the majority class, and generating synthetic samples using techniques such as SMOTE (Synthetic Minority Over-sampling Technique).
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