Mission accomplished

It’s often the data that takes the blame for a poor-performing, uninterpretable, or biased model, and that can be true, but many different things can be done in the preparation and model development stages to improve it. To offer an analogy, it’s like baking a cake. You need quality ingredients, yes. But seemingly small differences in the preparation of these ingredients and baking itself—such as the baking temperature, the container used, and time—can make a huge difference. Hell! Even things that are out of your control, such as atmospheric pressure or moisture, can impact baking! Even after it’s all finished, how many different ways can you assess the quality of a cake?

This chapter is about these many details, and, as with baking, they are part exact science and part art form. The concepts discussed in this chapter also have far-reaching consequences, especially regarding how to optimize a problem that doesn’t...