How do we know whether the model we have trained is a good model? We will need to be able to evaluate its predictive performance in some way. Evaluation metrics are measures of a model's predictive capability or accuracy. Some are direct measures of how well a model predicts the model's target variable, such as Mean Squared Error, while others are concerned with how well the model performs at predicting things that might not be directly optimized in the model, but are often closer to what we care about in the real world, such as Mean Average Precision.
Evaluation metrics provide a standardized way of comparing the performance of the same model with different parameter settings and of comparing performance across different models. Using these metrics, we can perform model selection to choose the best-performing model from the set of models we wish to evaluate...