支持向量机回归

用于回归模型的支持向量机

为了提高在中低维数据集上的准确度，可以使用 fitrsvm 训练支持向量机 (SVM) 模型。

为了减少在高维数据集上的计算时间，可以使用 fitrlinear 高效地训练线性回归模型，例如线性 SVM 模型。

App

使用有监督机器学习训练回归模型来预测数据

使用支持向量机 (SVM) 回归模型预测响应

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

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

`fitrkernel`	Fit Gaussian kernel regression model using random feature expansion
`predict`	Predict responses for Gaussian kernel regression model

crossval Cross-validated support vector machine regression model

`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

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

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

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

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.
Train Kernel Approximation Model Using Regression Learner App
Create and compare kernel approximation models, and export trained models to make predictions for new data.