Supervised Learning

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. A test dataset is often used to validate the model. Using larger training datasets often yield models with higher predictive power that can generalize well for new datasets.

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 Neural Network 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: MATLAB, Statistics and Machine Learning Toolbox, statistics and machine learning toolbox, neural network toolbox, machine learning, unsupervised learning, adaboost, linear regression, nonlinear regression, data fitting, data analysis, mathematical modeling, Predictive Modeling

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