Feature Engineering for Modern Machine Learning with Scikit-Learn

5.4 What Could Go Wrong?

Addressing imbalanced data is crucial for building effective machine learning models, especially in fields like fraud detection and medical diagnosis where class imbalances are common. However, techniques like SMOTE, class weighting, and specific cross-validation methods can present challenges if not implemented carefully. Here are some potential pitfalls and strategies to mitigate them.

5.4.1 Overfitting from Excessive Oversampling with SMOTE

While SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class, oversampling can lead to overfitting, particularly in small datasets. Overfitting occurs when the model “memorizes” the synthetic samples, which may be too similar to existing ...