Supervised learning

Supervised learning is the more common of the two types, and, as such, it is also used for most of the algorithms we will cover in this book. Simply put, supervised learning entails you, as the data scientist, passing the known outputs as part of the training to the model. Take, for instance, the election example discussed earlier in this chapter. With supervised learning, every data point in the election polls that is used as a feature along with whom they say will vote for, are sent to the model during training. This step is traditionally called labeling in classification algorithms, in which the output values will be one of the pre-training labels.