When a model is under-performing, it is often not clear how to make it better. Throughout this book, I have declared a rule of thumbs, for example, how to select the number of layers in a neural network. Even worse, the answer is often counter-intuitive! For example, adding another layer to the network might make the results worse, and adding more training data might not change performance at all.
You can see why these issues are some of the most important aspects of machine learning. At the end of the day, the ability to determine what steps will or will not improve our model is what separates the successful machine learning practitioner from all others.
Let's have a look at a specific example. Remember Chapter 5 ...
