How to deal with class imbalance?

Class imbalance is a situation where some classes are represented much more frequently than others in the dataset. This can lead to the model being biased towards the majority class and performing poorly on rare classes.

Main methods to combat imbalance:

• Resampling:

  • Oversampling — increasing the number of examples of the rare class (e.g., using SMOTE).
  • Undersampling — reducing the number of examples of the dominant class.

• Using special metrics: instead of accuracy, apply F1-score, ROC-AUC, Precision-Recall.

• Class weighting: assigning higher weights to errors on rare classes during model training.

• Data augmentation: creating new examples for rare classes.

• Using algorithms that are robust to imbalance: for example, ensemble methods, gradient boosting with parameters for imbalance.

Example of using class weighting in scikit-learn:

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(class_weight='balanced')
clf.fit(X_train, y_train)

This approach helps the model pay more attention to rare classes.