Summary
The challenge of imbalanced datasets in machine learning often results in biased predictions and compromised model outcomes. This chapter delves deep into the complexities of such datasets and illuminates the path through conformal prediction, a groundbreaking approach to handling these scenarios.
Traditional methods, such as resampling techniques, and metrics, such as ROC AUC, often fail to address the imbalances effectively. Furthermore, they can sometimes lead to even more skewed results. On the other hand, conformal prediction emerges as a robust solution, offering calibrated and reliable probability estimates.
The practical implications of these methods are illustrated using the Credit Card Fraud Detection dataset from Kaggle, an inherently imbalanced dataset. The exploration underscores the significance of understanding the data, using robust metrics, and the transformative potential of conformal prediction.
In essence, while imbalanced data presents challenges...
