Minority Class
In data science, minority class refers to the class or category in a dataset that has significantly fewer instances or observations than the majority class. This is a common problem in many real-world datasets, where the minority class may represent a rare event or a group of interest. The imbalanced distribution of classes can lead to biased models that perform poorly in predicting the minority class. To address this issue, various techniques have been developed, such as oversampling the minority class, undersampling the majority class, or generating synthetic samples using techniques like SMOTE (Synthetic Minority Over-sampling Technique). These techniques aim to balance the class distribution and improve the performance of the model on the minority class.
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