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Examples of imbalanced data set in machine learning

In machine learning it's very important to handle the imbalanced data set. Here are some examples. There could be many more.

  1. Credit Card Fraud Detection: In credit card transactions, fraudulent cases are rare compared to legitimate transactions. Resampling and cost-sensitive learning techniques are commonly used to address this imbalance.


  2. Medical Diagnosis: Rare diseases are a challenge in medical diagnosis. Techniques like synthetic data generation or anomaly detection can help improve the accuracy of detecting such diseases.


  3. Manufacturing Quality Control: In manufacturing, detecting rare defects in products is crucial. Collecting more data on defect cases and using anomaly detection can help maintain product quality.


  4. Email Spam Detection: In email filtering, spam emails are much less frequent than legitimate emails. Adjusting classification thresholds and using resampling techniques can improve spam detection.


  5. Intrusion Detection in Networks: Detecting rare network intrusions among normal traffic is a common problem. Anomaly detection algorithms and ensemble methods are used for this purpose.

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