How do you handle imbalanced datasets?

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Handling imbalanced datasets is crucial when one class has far fewer samples than others, as models may become biased toward the majority class.

Techniques to Handle Imbalance:

1. Resampling Methods

• Oversampling minority class (e.g., SMOTE – Synthetic Minority Over-sampling Technique).

• Undersampling majority class to balance counts.

2. Use Appropriate Metrics

• Accuracy can be misleading; use Precision, Recall, F1-score, ROC-AUC instead.

3. Algorithmic Approaches

• Use models that handle imbalance well (e.g., Random Forest, XGBoost with scale_pos_weight).

• Adjust class weights to penalize misclassification of minority class.

4. Data Augmentation

• Create synthetic samples (especially in image/text datasets).

5. Anomaly Detection Techniques

• Treat the minority class as anomalies when imbalance is extreme.

Example (Class Weights in Scikit-learn):

from sklearn.linear_model import LogisticRegression
model = LogisticRegression(class_weight='balanced')

In short: Handle imbalanced datasets by resampling, using proper evaluation metrics, adjusting model weights, or generating synthetic data to ensure fair learning across all classes.

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