I’m working on a machine learning project where I’m trying to evaluate the performance of different models. I know that accuracy is an important metric, but I’ve also heard that it’s not always the best metric to use, especially in situations where the dataset is imbalanced or where false positives/negatives have different costs.
Can someone provide some guidance on other evaluation metrics that I should consider beyond accuracy? And are there any situations where accuracy might not be a good metric to use at all?
This is the dataset that I am using:
Any help would be greatly appreciated! Thanks in advance.