## Supervised Learning |

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 (
*k*NN)

Common regression algorithms include:

- Linear regression
- Nonlinear regression
- Generalized linear models
- Decision trees
- Neural networks

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.

- An Introduction to Classification 9:00 (Video)
- Regression Analysis with MATLAB: New Statistics Toolbox Capabilities in R2012a 35:09 (Webinar)
- Classifying Fisher's Iris Data (Example)
- Credit Rating by Bagging Decision Trees (Example)
- K-Nearest Neighbor Classification (Example)
- Credit Risk Modeling with MATLAB 53:09 (Webinar)
- Electricity Load and Price Forecasting with MATLAB 47:43 (Webinar)
- Machine Learning with MATLAB 3:02 (Video)

- Supervised Learning (Workflow and Algorithms) (Documentation)
- Support Vector Machines (Documentation)
- Nonlinear Regression (Documentation)
- fitensemble: Create ensemble of bagged decision trees (Function)
- svmclassify: Classify using support vector machine (Function)

*See also*: *statistics and machine learning toolbox*, *neural network toolbox*, *machine learning*, *unsupervised learning*, *adaboost*, *linear regression*, *nonlinear regression*, *data fitting*, *data analysis*, *mathematical modeling*