A model needs to be evaluated on unseen data to assess its goodness. The term goodness may be expressed in several ways and these ways are termed as model performance metrics.

Several metrics exist to report the performance of models. Accuracy, precision, recall, F-score, sensitivity, specificity, AUROC curve, root mean squared error (RMSE), Hamming loss, and mean squared error (MSE) are some of the popular model performance metrics among others.