ML0019 Imbalanced Data

Handling imbalanced data in machine learning involves addressing scenarios where one class significantly outnumbers the other, which can skew model performance. Here are common techniques:

Dataset Resampling:
Oversampling: Increase the minority class samples (e.g., using SMOTE or ADASYN to generate synthetic data points).
Undersampling: Reduce the majority class samples to balance the dataset.

Data Augmentation:
Create synthetic data for the minority class with data augmentation techniques.

Class Weights Adjustment:
Assign higher weights to the minority class during training to penalize misclassifications more heavily.

Metrics Selection:
Use evaluation metrics like Precision, Recall, F1 Score, or AUC-ROC rather than accuracy.