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

回归学习器

Train regression models to predict data using supervised machine learning

模块

RegressionSVM Predict

Predict responses using support vector machine (SVM) regression model

函数

全部展开

SVM

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

线性回归

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

核回归

`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

改进回归

crossval Cross-validated support vector machine regression model

解释回归

`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

类

全部展开

SVM

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

线性回归

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

核回归

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

主题

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.