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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