Dealing with Edge Cases and Rare Events in Machine Learning

In the field of machine learning, it is crucial to identify and handle edge cases properly. Edge cases refer to instances in your dataset that are significantly different from the majority of the data, and they can have a substantial impact on the performance and reliability of your machine learning models. Rare events can be challenging for machine learning models due to class imbalance problems as they might not have enough data to learn patterns effectively. Class imbalance occurs when one class (the rare event) is significantly underrepresented compared to the other class(es). Traditional machine learning algorithms tend to perform poorly in such scenarios because they may be biased toward the majority class, leading to lower accuracy in identifying rare events.

In this chapter, we will explore various techniques and approaches to detect edge cases in machine learning and data, using Python code examples. We’...