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How do you address class imbalance in a dataset?

A class imbalance is a challenge that often arises in machine learning tasks, especially when one class outnumbers the other. This imbalance can result in biased models which favor the majority while not correctly classifying the minority. This issue calls for strategic approaches that will allow the model to learn from both classes and improve its performance. https://www.sevenmentor.com/da....ta-science-course-in

Resampling is a fundamental technique to handle class imbalance. Oversampling of the minority class is one way to handle class imbalance. Synthetic minorities are generated using the Synthetic Minority over-sampling Technique. SMOTE generates synthetic samples through interpolation between existing data, enhancing learning of models without duplicating records. In the opposite direction, undersampling decreases the number instances of the majority class, making the dataset balanced. This method, however, risks losing valuable data, which can impact model accuracy.