As we said earlier, supervised learning algorithms learn to approximate a function by mapping inputs and outputs to create a model that is able to predict future outputs given unseen inputs.
It's conventional to denote inputs as x
and outputs as y;
both can be numerical or categorical.
We can distinguish them as two different types of supervised learning:
- Classification
- Regression
Classification is a task where the output variable can assume a finite amount of elements, called categories. An example of classification would be classifying different types of flowers (output) given the sepal length (input). Classification can be further categorized in more sub types:
- Binary classification: The task of predicting whether an instance belongs either to one class or the other
- Multiclass classification: The task (also known as multinomial...
