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How to handle imbalanced data set in machine learning problem

TechniqueDescriptionReal-Life Example
Resampling- Oversampling: Increase the number of minority class samples. - Undersampling: Reduce the number of majority class samples.Example: In fraud detection, where fraudulent transactions are rare, you can oversample the minority class to balance the dataset. Conversely, you can undersample non-fraudulent transactions.
Synthetic DataGenerate synthetic samples for the minority class using techniques like SMOTE (Synthetic Minority Over-sampling Technique).Example: In medical diagnosis, when positive cases are scarce, generate synthetic data points to improve model accuracy.
Cost-Sensitive LearningModify the algorithm's objective function to penalize misclassification of the minority class more than the majority class.Example: In healthcare, misdiagnosing a rare disease may be costlier, so the algorithm can be tuned to minimize such errors.
Ensemble MethodsCombine predictions from multiple models to improve performance, e.g., Random Forests, AdaBoost, or XGBoost.Example: In credit scoring, ensemble methods can help balance recall and precision when dealing with rare default cases.
Anomaly DetectionTreat the minority class as anomalies and use anomaly detection algorithms like Isolation Forest or One-Class SVM.Example: In network security, detecting rare intrusions among legitimate traffic patterns.
Change the ThresholdAdjust the classification threshold to increase sensitivity or specificity based on the problem's requirements.Example: In email spam detection, lowering the threshold may increase the recall of spam emails.
Collect More DataSometimes, collecting more data for the minority class may be a practical solution if feasible.Example: In manufacturing, if defective products are rare, collecting more data on defect cases can help.
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