When dealing with a classification or a regression problem, we tend to start by thinking about the features we should include in our models. Nonetheless, it is often that the key to the solution lies in the target values. As we have seen in this chapter, rescaling our regression target can help us use a simpler model. Furthermore, calibrating the probabilities given by our classifiers may quickly give a boost to our accuracy scores and help us quantify our uncertainties. We also learned how to deal with multiple targets by writing a single estimator to predict multiple outputs at once. This helps to simplify our code and allows the estimator to use the knowledge it learns from one label to predict the others.
It is common in real-life classification problems that classes are imbalanced. When detecting fraudulent incidents, the majority of your data is usually comprised of non-fraudulent cases. Similarly, for problems such as who would click on your advertisement...
