Supervised Learning

Supervised learning takes a set of input-output pairs and learns a function that maps the input to the output. The algorithm is trained on a set of labeled data, meaning the output is known. The goal is to learn a function that can make predictions on new data.

Types of Supervised Learning

There are multiple types of supervised learning algorithms.

Regression

Regression is used when the output is a continuous value. The goal is to learn a function that can predict the output for new input data.

Example: Predicting the price of a house based on its size (square meters).

See more: Linear Regression

Classification

Classification is used when the output is a discrete value. The goal is to learn a function that can predict the class of new input data.

Example: Predicting if an email is spam or not.

See more: Logistic Regression

Terminology

  • Feature: An input variable used to make predictions.
  • Target: The output variable that we want to predict.
  • Training Set: The set of input-output pairs used to train the algorithm.
  • Test Set: The set of input-output pairs used to evaluate the algorithm.
  • Learning Algorithm: The algorithm used to learn the function that maps the input to the output.
  • Model: The function learned by the algorithm.
  • Prediction: The output of the model for a given input.