Machine learning technique for building predictive models from known input and response data

Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. The training dataset includes input data and response values. From it, the supervised learning algorithm seeks to build a model that can make predictions of the response values for a new dataset. Using larger training datasets and optimizing model hyperparamamters can often increase the model’s predictive power and ensure that it can generalize well for new datasets. A test dataset is often used to validate the model.

Supervised learning includes two categories of algorithms:

  • Classification: for categorical response values, where the data can be separated into specific “classes”
  • Regression: for continuous-response values

Common classification algorithms include:

  • Support vector machines (SVM)
  • Neural networks
  • Naïve Bayes classifier
  • Decision trees
  • Discriminant analysis
  • Nearest neighbors (kNN)

Common regression algorithms include:

For more details on supervised learning algorithms, see Statistics and Machine Learning Toolbox™ and Deep Learning Toolbox™.

Supervised learning is used in financial applications for credit scoring, algorithmic trading, and bond classification; in biological applications for tumor detection and drug discovery; in energy applications for price and load forecasting; and in pattern recognition applications for speech and images.

See also: Statistics and Machine Learning Toolbox, Deep Learning Toolbox, machine learning, unsupervised learning, AdaBoost, linear regression, nonlinear regression, data fitting, data analysis, mathematical modeling, predictive modeling, artificial intelligence, AutoML

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