Support Vector Machine Regression

Support vector machines for regression models

For greater accuracy on low- through medium-dimensional data sets, train a support vector machine (SVM) model using fitrsvm.

For reduced computation time on high-dimensional data sets, efficiently train a linear regression model, such as a linear SVM model, using fitrlinear.

Apps

Regression Learner

Train regression models to predict data using supervised machine learning

Blocks

RegressionSVM Predict

Predict responses using support vector machine (SVM) regression model

Functions

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SVM

`fitrsvm`	Fit a support vector machine regression model
`predict`	Predict responses using support vector machine regression model

Linear Regression

`fitrlinear`	Fit linear regression model to high-dimensional data
`predict`	Predict response of linear regression model

Kernel Regression

`fitrkernel`	Fit Gaussian kernel regression model using random feature expansion
`loss`	Regression loss for Gaussian kernel regression model
`predict`	Predict responses for Gaussian kernel regression model
`resume`	Resume training of Gaussian kernel regression model

Improve Regression

crossval Cross-validated support vector machine regression model

Interpret Regression

`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`lime`	Local interpretable model-agnostic explanations (LIME)
`shapley`	Shapley values

Classes

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SVM

`RegressionSVM`	Support vector machine regression model
`CompactRegressionSVM`	Compact support vector machine regression model

Linear Regression

`RegressionLinear`	Linear regression model for high-dimensional data
`RegressionPartitionedLinear`	Cross-validated linear regression model for high-dimensional data

Kernel Regression

`RegressionKernel`	Gaussian kernel regression model using random feature expansion
`RegressionPartitionedKernel`	Cross-validated kernel model for regression

Topics

Predict Responses Using RegressionSVM Predict Block

Train a support vector machine (SVM) regression model using the Regression Learner app, and then use the RegressionSVM Predict block for response prediction.

Understanding Support Vector Machine Regression

Understand the mathematical formulation of linear and nonlinear SVM regression problems and solver algorithms.