Supervised versus unsupervised learning models

We have already discussed the concept of target (dependent) variables and independent variables, or features. Features (or independent variables) are used to describe the relationship with, or to predict values of, a target variable. After defining your independent and depending variables, you will formulate your model. One way to characterize the way in which a model learns from the data, is by classifying it into either a supervised or unsupervised learning model.

Supervised learning models

When the possible values of a target variable are specified and labeled, a model is considered supervised, that is, we know what we want to predict, and the goal is to find the most appropriate predictive model which will predict the outcome.

As an example, if we are predicting the approval rating for a product, we know what we are predicting (approval rating of a product), and we also usually know the range of possible outcomes. It could be a percentage from...