Supervised Learning
- In order to train a machine learning model, we need to provide it with training data.
- The training data consists of a set of instances. Each instance is a pair of an input and an output. The input is a set of features and the output is a label.
- The goal of supervised learning is to learn a function that maps the input to the output.
Info
The term feature is used interchangeably with the term attribute. An attribute usually denotes a property name only while a feature denotes a property name and value.
The output variable can either be a regression or a classification value.
Examples
The following are examples of machine learning algorithms that use supervised learning: