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Handling imbalanced datasets

Instruction and application
In Progress

Picture building a model to identify defective products when almost every item passes inspection. The model achieves impressive accuracy by predicting “no defect” nearly every time — yet it misses the rare, critical cases that truly matter.

Not all datasets are created equal. In many real-world problems, from fraud detection to medical diagnosis to predictive maintenance, the outcomes that matter most are often the rarest. Handling class imbalance helps your model stay both fair and accurate across all possible outcomes.

In this section, you’ll explore what imbalance means, why it can mislead even the best-performing models, and practical ways to correct it.

Handling imbalanced datasets

What imbalance means and why it matters​

Class imbalance occurs when one class (the majority) vastly outnumbers the other (the minority).

For example, in a dataset with 10,000 transactions, if only 200 are fraudulent, the dataset is 98% negative and 2% positive. If we rely solely on accuracy, a model that predicts no fraud for every transaction would appear**98% accurate,**but completely ineffective.This imbalance can cause models to:

  • Focus excessively on the majority class and ignore rare but important cases.
  • Produce inflated accuracy scores that hide poor minority-class performance.
  • Fail in real-world applications where the minority class drives critical decisions..

Effects of imbalance on learning and evaluation metrics​

When the data is imbalanced, models struggle to learn from underrepresented examples, and traditional metrics like accuracy no longer reflect true performance. Key metrics that better capture imbalance include:

  • Precision: Of all instances predicted as positive, how many were actually positive? High precision means fewer false alarms.
  • Recall (sensitivity): Of all actual positive instances, how many did the model detect? High recall means fewer missed cases.
  • F1-score: The harmonic mean of precision and recall, providing a single metric that balances the two. Useful when you need to balance false positives and false negatives.

Strategies to address imbalance​

Once imbalance is detected, several strategies can help restore fairness and reliability:

Resampling methods​

  • Oversampling: Increases the number of minority class samples (e.g., by duplication).
  • Undersampling: Reduces majority class samples to balance proportions. While simple, these methods can introduce bias or cause overfitting if not used carefully.

Synthetic data generation​

  • Techniques such as SMOTE (Synthetic Minority Over-sampling Technique) andADASYN create synthetic minority examples based on nearest neighbors.
  • They add realistic diversity to underrepresented data points, helping the model learn broader patterns.

Class weighting in algorithms​

  • Most modern algorithms (e.g., Logistic Regression, Random Forest, XGBoost) allow you to assign higher weights to minority classes, penalising errors on rare outcomes more heavily.
  • This approach avoids altering the dataset while still encouraging the model to treat all classes fairly.

Combining sampling and algorithmic adjustments​

  • The best approach often blends both strategies, e.g., modest oversampling with tuned class weights to balance data without distorting it.

Action item: Reflection – Balancing for fairness​

Take a few minutes to consider the following question.

  • What type of imbalance challenges are most common in your work — and which balancing approach would you test first?
  • How might using precision, recall, and F1-score instead of accuracy change how you evaluate your model’s success?