1.2.5. Testing Model

The metrics used to evaluate quality in the process of ML modeling depend on the nature of the model, which may be for classification or regression. In regression models, the metrics quantify the reliability of the model and the error between the model output and the real-world system. The most common regression metrics are the root mean square error (RMSE), mean error (ME), mean absolute error (MAE), mean average percentage error (MAPE), and the Nash coefficients *E* and *R*<sup>2</sup> [22,44].
