Choosing evaluation metrics

When dealing with edge cases and rare events in machine learning, selecting the right evaluation metrics is crucial to accurately assess the performance of the model. Traditional evaluation metrics, such as accuracy, may not be sufficient in imbalanced datasets where the class of interest (the rare event) is vastly outnumbered by the majority class. In imbalanced datasets, where the rare event is a minority class, traditional evaluation metrics such as accuracy can be misleading. For instance, if a dataset has 99% of the majority class and only 1% of the rare event, a model that predicts all instances as the majority class will still achieve an accuracy of 99%, which is deceptively high. However, such a model would be ineffective in detecting the rare event. To address this issue, we need evaluation metrics that focus on the model’s performance in correctly identifying the rare event, even at the expense of a decrease in accuracy.

Here are some...