Metrics

The metric chosen to evaluate the algorithm is another extremely important step in the machine learning process. You can also choose one particular metric as the loss of the algorithm aims to minimize. The loss is a measure of the error that our algorithm produces if we compare its predictions to our ground truth. The loss is very important as it determines how the algorithm will evaluate its mistakes and therefore how it will learn the function that maps the inputs with the outputs.

We can divide again the metrics by the type of problems we have, metrics for classification, or regression.