支持向量机回归 -凯发k8网页登录

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
	使用线性回归模型预测响应

`fitrsvm`	fit a support vector machine regression model
	predict responses using support vector machine regression model

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

	fit gaussian kernel regression model using random feature expansion
	predict responses for gaussian kernel regression model

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
	cross-validated linear regression model for high-dimensional data

	gaussian kernel regression model using random feature expansion
	cross-validated kernel model for regression

understand the mathematical formulation of linear and nonlinear svm regression problems and solver algorithms.
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.
this example shows how to use the regressionlinear predict block for response prediction in simulink®.