Models#

In supervised learning, we have a dataset consisting of both features (usually multiple X variables) and labels (our y variable). The task is to construct an estimator (model) which is able to predict the label y of an object given the set of features X.

Supervised learning is further broken down into two categories:

  • classification

  • regression

In classification, the label is discrete, while in regression, the label is continuous.

Resources