Class imbalance occurs when some classes in your dataset have significantly fewer examples than others, causing models to perform poorly on underrepresented groups. The first step is recognizing the problem through metrics like class distribution checks. For example, in a fraud detection dataset with 99% legitimate transactions and 1% fraud, a model predicting “not fraud” every time would achieve 99% accuracy but fail to detect fraud. To address this, you can apply resampling techniques. Oversampling the minority class (e.g., duplicating fraud examples) or undersampling the majority class (e.g., randomly removing legitimate transactions) balances the dataset. Tools like Python’s imbalanced-learn library provide methods like RandomOverSampler or SMOTE (which generates synthetic minority samples). However, oversampling risks overfitting to noise, while undersampling discards potentially useful data, so experiment with combinations like SMOTE followed by light undersampling.
Another approach is adjusting class weights during model training. Many algorithms, such as logistic regression or random forests, allow assigning higher penalties for misclassifying minority classes. For instance, setting class_weight='balanced' in scikit-learn’s RandomForestClassifier tells the model to prioritize minority class accuracy. Evaluation metrics also matter: avoid accuracy and instead use precision, recall, F1-score, or AUC-ROC. For example, in medical diagnosis (where false negatives are critical), optimizing for recall ensures fewer missed cases. You can also threshold adjustments—shifting the decision boundary to favor minority class predictions. For example, lowering the probability threshold from 0.5 to 0.3 for classifying a rare disease might increase true positives at the cost of more false positives.
Advanced techniques include using ensemble methods designed for imbalance, such as EasyEnsemble or BalancedRandomForest, which combine resampling with bagging. For extreme imbalances (e.g., 1:10,000 ratios), anomaly detection frameworks like isolation forests or one-class SVMs might treat the minority class as outliers. Data augmentation (e.g., image rotation for a small visual class) or collecting more samples for rare classes can also help. Always validate using stratified cross-validation to ensure minority class representation in splits. For example, splitting a dataset with 5% minority samples using 5-fold CV ensures each fold retains that 5%. There’s no universal solution—test multiple strategies and measure their impact using domain-specific metrics.