Summary
In this chapter we observed that some of the evaluation metrics for classification models require a binary classification model. We saw that when we worked with more than two classes, we were required to use the one-versus-all approach. The one-versus-all approach builds one model for each class and tries to predict the probability that the input belongs to a specific class. We saw that once this was done, we then predicted that the input belongs to the class where the model has the highest prediction probability. We also split our evaluation dataset into two, it's because X_test and y_test are used once for a final evaluation of the model's performance. You can make use of them before putting your model into production to see how the model would perform in a production environment.
You have learned how to assess the quality of a regression model by observing how the loss changes. You saw examples using the MAE, and also learned of the existence of MSE...
