One challenge is that different evaluation metrics prioritize different aspects of model performance, so it's important to understand the specific objective of your machine learning task. For example, accuracy may be suitable for balanced datasets, but not for imbalanced ones where other metrics like precision or recall might be more appropriate. Another challenge is that evaluation metrics don't always capture the full picture of a model's performance, and it's crucial to consider additional factors like business requirements and practical implications.

Choosing evaluation metrics is a crucial step in the machine learning process. One common challenge is the trade-off between different metrics. For instance, optimizing for accuracy may lead to overfitting and poor generalization. Additionally, depending on the problem domain, certain metrics may be more relevant than others. For instance, in medical diagnosis, false negatives may be more dangerous than false positives, so metrics like sensitivity become crucial. Ultimately, choosing the right evaluation metrics requires careful consideration of the problem at hand and the trade-offs involved.