Supervised learning is a type of machine learning where the algorithm learns from labeled training data. It is called "supervised" because the process of learning from the training data is supervised. The goal of supervised learning is to predict the output for new data given the input.

Key Components of Supervised Learning

  1. Input (X): The input features that the algorithm uses to make predictions.
  2. Output (Y): The target variable that the algorithm tries to predict.
  3. Training Data: A set of labeled data that the algorithm uses to learn.
  4. Model: The algorithm that learns from the training data.

Types of Supervised Learning

  1. Classification: The algorithm predicts a categorical output. For example, determining whether an email is spam or not.
  2. Regression: The algorithm predicts a continuous output. For example, predicting the price of a house.
  3. Binary Classification: A special case of classification where the output is binary (e.g., yes/no, true/false).

Example

Let's say you want to build a model to predict whether an email is spam or not. You would need a dataset of emails that are labeled as spam or not spam. The input features could include the sender's email address, the subject line, and the content of the email. The output would be a binary value indicating whether the email is spam or not.

Related Resources

Supervised Learning Diagram