Understand the challenges posed by imbalanced datasets, explore techniques such as oversampling, undersampling, SMOTE (Synthetic Minority Over-sampling Technique), and class weight adjustments to address class imbalance for machine learning models.
Handling Imbalanced Data 1. Understanding Imbalanced Data: Imbalanced data occurs when one class in a classification problem has significantly more instances than the other classes. This can lead to biased models that favor the majority class and perform poorly on the minority class. 2. Techniques for Handling Imbalanced Data: 2.1. Resampling Techniques: Over-sampling: Increase the number of in...…
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