In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into:
- Regression problems, and
- Classification problems
In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.
In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.