Imbalanced Data

Imbalanced Data

Imbalanced Data refers to datasets where the classes are not represented equally. In classification problems, one class (usually the positive or minority class) has far fewer examples than the other class (negative or majority class).

Why is Imbalanced Data a Problem?

Machine learning models often assume that classes are balanced and try to maximize overall accuracy. When data is imbalanced, models tend to be biased toward the majority class, ignoring the minority class, which is often the more important one.

For example, in fraud detection, fraudulent transactions are rare but critical to detect. A model predicting all transactions as non-fraudulent might achieve high accuracy but be useless.

Challenges Caused by Imbalanced Data

• Poor detection of minority class (low recall).
• Misleading evaluation metrics like accuracy.
• Models may learn to ignore rare classes.
• Difficulty in learning decision boundaries.

How to Handle Imbalanced Data

Data-Level Techniques

• Resampling:
  • Oversampling: Increase minority class examples (e.g., using SMOTE - Synthetic Minority Over-sampling Technique).
  • Undersampling: Reduce majority class examples to balance dataset.
  • Combination: Both oversampling and undersampling for balance.

Algorithm-Level Techniques

• Cost-Sensitive Learning: Assign higher penalty to misclassifying minority class.
• Adjusting Decision Threshold: Tune classification threshold to favor minority class.
• Using Specialized Algorithms: Algorithms designed to handle imbalance (e.g., Balanced Random Forest).

Evaluation Metrics for Imbalanced Data

Accuracy is not a good measure here. Instead, use:

• Precision
• Recall
• F1 Score
• Area Under Precision-Recall Curve (AUPRC)
• Confusion Matrix analysis

Example

Suppose a dataset has 95% negative (non-disease) and 5% positive (disease) cases. A naive model predicting all cases as negative achieves 95% accuracy but fails to detect any disease case (Recall = 0%).

Using oversampling or cost-sensitive methods improves detection of the minority disease class.

Related Pages

• Precision
• Recall
• F1 Score
• Confusion Matrix
• Precision-Recall Curve
• Threshold Tuning
• Cost-Sensitive Learning

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