Supervised learning is a type of machine learning where an algorithm learns from a labeled dataset. The goal is to learn a mapping from input variables (X) to output variables (Y). The algorithm tries to learn a function that maps the input to the correct output.

Key Concepts

  • Training Data: A dataset with input-output pairs used to train the model.
  • Features: Input variables used to train the model.
  • Labels: Output variables that the model tries to predict.
  • Model: The algorithm that learns from the training data.

Types of Supervised Learning

  • Classification: Predicting a categorical outcome. Example: Determine whether an email is spam or not.
  • Regression: Predicting a continuous outcome. Example: Predicting the price of a house.

Common Algorithms

  • Linear Regression: A simple model that predicts a continuous outcome using a linear relationship between the features and the output.
  • Logistic Regression: A model that predicts a binary outcome using a logistic function.
  • Support Vector Machines (SVM): A model that separates data points into different classes using a hyperplane.
  • Neural Networks: A complex model that consists of multiple layers of interconnected neurons.

Machine Learning Model

For more information on supervised learning, check out our Introduction to Machine Learning.

Challenges

  • Overfitting: The model performs well on the training data but poorly on unseen data.
  • Underfitting: The model performs poorly on both the training and unseen data.

Overfitting and Underfitting

To overcome these challenges, it's important to use techniques like cross-validation and regularization.

For further reading on supervised learning, you might also be interested in our Advanced Machine Learning Techniques.