Misleading Accuracy Metrics

Accuracy can be a deceptive metric when evaluating machine learning algorithms, especially in cases of imbalanced datasets. A classifier might achieve high accuracy by simply labeling most inputs as legitimate, failing to effectively identify spam. This highlights the importance of using more nuanced evaluation methods to truly assess algorithm performance.