Supervised learning is a type of machine learning where an algorithm learns from labeled training data. The goal is to predict the output based on the input data. This is one of the most common types of machine learning and is widely used in various fields.

Key Concepts

  • Training Data: Data that is used to train the model. It should be labeled, meaning each data point has a corresponding output.
  • Model: The algorithm that learns from the training data and makes predictions.
  • Prediction: The output generated by the model for a given input.

Types of Supervised Learning

  1. Linear Regression: Used for predicting continuous values.
  2. Logistic Regression: Used for predicting binary outcomes.
  3. Support Vector Machines (SVM): Used for both classification and regression tasks.
  4. Neural Networks: Used for complex tasks and can be used for both classification and regression.

Example

Suppose you want to predict whether a student will pass or fail an exam based on their study hours. You can use a supervised learning algorithm to train a model on historical data where the input is the number of study hours and the output is whether the student passed or failed.

Further Reading

For more information on supervised learning, you can visit our Machine Learning Resources.

Supervised Learning