What Are Some Techniques to Handle Class Imbalance?
Several techniques have been developed to address class imbalance. These include:
Resampling Methods: These involve over-sampling the minority class or under-sampling the majority class to create a more balanced dataset. Algorithmic Approaches: Certain algorithms inherently handle imbalance better, such as decision trees and random forests. Additionally, modifications to regular algorithms, like adjusting class weights, can help. Ensemble Methods: Techniques like boosting can help improve model performance by focusing on difficult-to-classify examples. Synthetic Data Generation: Methods such as SMOTE (Synthetic Minority Over-sampling Technique) create synthetic examples to balance the dataset.
