A lot of attention is given to the input features, that is, our x's. We have used algorithms to scale them, select from them, and engineer new features to add to them. Nonetheless, we should also give as much attention to the targets, the y's. Sometimes, scaling your targets can help you use a simpler model. Some other times, you may need to predict multiple targets at once. It is, then, essential to know the distribution of your targets and their interdependencies. In this chapter, we are going to focus on the targets and how to deal with them.
In this chapter, we will cover the following topics:
- Scaling your regression targets
- Estimating multiple regression targets
- Dealing with compound classification targets
- Calibrating a classifier's probabilities
- Calculating the precision at K
