Explain the confusion matrix and derive precision, recall, F1 score, and accuracy from it. When would you prioritize each metric, especially in imbalanced datasets?

Sample answer preview

The confusion matrix is a table that visualizes classification model performance by comparing predicted labels against actual labels. It provides the foundation for calculating various evaluation metrics. The confusion matrix has four components for binary classification.