In this chapter, we modeled a somewhat complex set of data for us to optimize the money that was spent on the given ad campaign. In the beginning of this book, I foreshadowed that we would be discussing measuring machine learning via profit. This is a great example of it. By combining multiple techniques, we can create models suited to solving real-world problems. On top of this, we saw some more ways of working with sklearn that prevents the coupling of your code with sklearn tightly.
Moving on from here, you can expect to spend less time manually implementing machine learning algorithms, and spending more time learning to use sklearn's built-in models. We haven't even tapped sklearn's pipeline features, nor its wide array of tunable parameters for the many machine learning algorithms that it supports. In most of the classification models, sklearn supports providing you a probability of a given classification. As we saw in this chapter, this can be a powerful tool when combined with...
