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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 LearnerTrain regression models to predict data using supervised machine learning

Blocks

RegressionSVM PredictPredict responses using support vector machine (SVM) regression model

Functions

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fitrsvmFit a support vector machine regression model
predictPredict responses using support vector machine regression model
fitrlinearFit linear regression model to high-dimensional data
predictPredict response of linear regression model
fitrkernelFit Gaussian kernel regression model using random feature expansion
predictPredict responses for Gaussian kernel regression model
crossvalCross-validated support vector machine regression model
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
limeLocal interpretable model-agnostic explanations (LIME)
shapleyShapley values

Classes

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RegressionSVMSupport vector machine regression model
CompactRegressionSVMCompact support vector machine regression model
RegressionLinearLinear regression model for high-dimensional data
RegressionPartitionedLinearCross-validated linear regression model for high-dimensional data
RegressionKernelGaussian kernel regression model using random feature expansion
RegressionPartitionedKernelCross-validated kernel model for regression

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