Evaluation Metrics in Machine Learning

Knowing how to evaluate a model is very important. Candidates should understand metrics like accuracy, precision, recall, F1 score, AUC-ROC, log loss, mean absolute error, mean squared error, and R-squared. Also, understanding concepts like cross-validation, confusion matrix, and ROC curve is important.