What is accuracy and when is it misleading?

Accuracy = (TP + TN) / Total predictions — the fraction of predictions that are correct. It is easy to understand but misleading for imbalanced datasets. For example, if 95% of emails are not spam, a model that always predicts "not spam" achieves 95% accuracy but is useless. In such cases, metrics like Precision, Recall, F1-Score, AUC-ROC, or Matthews Correlation Coefficient (MCC) are more informative because they account for the distribution of classes.