In examples such as those in the previous section, the data consisted of some features and a target; no matter whether the target was quantitative (regression) or categorical (classification). Under these circumstances, we call the dataset a labeled dataset. When we try to produce a model from a labeled dataset in order to make predictions about unseen or future data (for example, to diagnose a new tumor case), we make use of supervised learning. In simple cases, supervised learning models can be visualized as a line. This line's purpose is to either separate the data based on the target (in classification) or to closely follow the data (in regression).

The following figure illustrates a simple regression example. Here, y is the target and x is the dataset feature. Our model consists of the simple equation y=2x-5. As is evident, the line closely follows the data. In order to estimate the y value of a new unseen point, we calculate its value using the preceding formula. The following figure shows a simple regression with y=2x-5 as the predictive model:

In the following figure, a simple classification problem is depicted. Here, the dataset features are x and y, while the target is the instance color. Again, the dotted line is y=2x-5, but this time we test whether the point is above or below the line. If the point's y value is lower than expected (smaller), then we expect it to be orange. If it is higher (greater), we expect it to be blue. The following figure is a simple classification with y=2x-5 as the boundary: