Answer / Nitin Gurjar

Handling an imbalanced dataset is crucial to ensure fair and accurate machine learning models. One common technique is to resample the data by either oversampling the minority class (positive samples) or undersampling the majority class (negative samples). Another approach is to use cost-sensitive learning, where misclassification costs are adjusted according to the imbalance in classes. Balanced evaluation metrics such as F1 score, precision, and recall can also help assess the performance of models on imbalanced datasets.

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