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 (Since R2020b)
RegressionLinear Predict	Predict responses using linear regression model (Since R2023a)
RegressionKernel Predict	Predict responses using Gaussian kernel regression model (Since R2024b)
IncrementalRegressionLinear Predict	Predict responses using incremental linear regression model (Since R2023b)
IncrementalRegressionLinear Fit	Fit incremental linear regression model (Since R2023b)
IncrementalRegressionKernel Fit	Fit incremental kernel regression model (Since R2024b)
IncrementalRegressionKernel Predict	Predict responses using incremental kernel regression model (Since R2024b)
Update Metrics	Update performance metrics in incremental learning model given new data (Since R2023b)
Detect Drift	Update drift detector states and drift status with new data (Since R2024b)

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
`predict`	Predict responses for Gaussian kernel regression model

Improve Regression

crossval Cross-validate machine learning model

Interpret Regression

`lime`	Local interpretable model-agnostic explanations (LIME) (Since R2020b)
`partialDependence`	Compute partial dependence (Since R2020b)
`permutationImportance`	Predictor importance by permutation (Since R2024a)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values (Since R2021a)

Multiresponse Regression with Regression Chains

`fitrchains`	Multiresponse regression with regression chains (Since R2024b)
`predict`	Predict responses using multiresponse regression model (Since R2024b)

Objects

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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

Multiresponse Regression with Regression Chains

`RegressionChainEnsemble`	Multiresponse regression model (Since R2024b)
`CompactRegressionChainEnsemble`	Compact multiresponse regression model (Since R2024b)

Topics

Understanding Support Vector Machine Regression
Understand the mathematical formulation of linear and nonlinear SVM regression problems and solver algorithms.
Train Kernel Approximation Model Using Regression Learner App
Create and compare kernel approximation models, and export trained models to make predictions for new data.
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.
Predict Responses Using RegressionLinear Predict Block
This example shows how to use the RegressionLinear Predict block for response prediction in Simulink®. (Since R2023a)