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:

1. Linear Regression
2. Logistic Regression
3. Support Vector Machine
4. Decision Tree
5. Random Forest
6. Neural Network
7. k-Nearest Neighbors