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回归学习者

Train regression models to predict data using supervised machine learning

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Description

这回归学习者app trains regression models to predict data. Using this app, you can explore your data, select features, specify validation schemes, train models, and assess results. You can perform automated training to search for the best regression model type, including linear regression models, regression trees, Gaussian process regression models, support vector machines, kernel approximation models, ensembles of regression trees, and neural network regression models.

通过提供一组已知的输入数据（预测因子）和已知响应的观察值来执行监督的机器学习。使用观察值训练一个模型，该模型生成了新输入数据的预测响应。要将模型与新数据一起使用或了解程序化回归，您可以将模型导出到工作区或生成MATLAB®代码重新出场d model.

Required Products

  • MATLAB

  • Statistics and Machine Learning Toolbox™

回归学习者app

Open the Regression Learner App

  • MATLAB Toolstrip: On theAppstab, underMachine Learning，单击应用程序图标。

  • MATLAB命令提示：输入regressionLearner。

Examples

Programmatic Use

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regressionLearneropens the Regression Learner app or brings focus to the app if it is already open.

回归线者（TBL,ResponseVarName)打开回归学习者应用程序，并从参数对话框中填充新的会话，并包含表中包含的数据TBL。这ResponseVarNameargument, specified as a character vector or string scalar, is the name of the variable inTBLthat contains the response values. The remaining variables inTBLare the predictor variables.

回归线者（TBL,Y)opens the Regression Learner app and populates the New Session from Arguments dialog box with the predictor variables in the tableTBLand the response values in the numeric vectorY。

regressionLearner (X, Y)opens the Regression Learner app and populates the New Session from Arguments dialog box with then-经过-ppredictor matrixXand then向量中的响应值Y。Each row ofXcorresponds to one observation, and each column corresponds to one variable. The length ofYand the number of rows ofXmust be equal.

回归线者（___，名称，值）specifies cross-validation options using one or more of the following name-value arguments in addition to any of the input argument combinations in the previous syntaxes. For example, you can specify“ kfold”，10to use a 10-fold cross-validation scheme.

  • "CrossVal", specified as"on"(default) or“离开”, is the cross-validation flag. If you specify"on", then the app uses 5-fold cross-validation. If you specify“离开”, then the app uses resubstitution validation.

    You can override the"CrossVal"cross-validation setting by using the"Holdout"或者“ kfold”name-value argument. You can specify only one of these arguments at a time.

  • "Holdout"，在[0.05,0.5]范围内指定为数字标量，是用于保留验证的数据的一部分。该应用程序使用剩余的数据进行培训（并指定）。

  • “ kfold”, specified as a positive integer in the range [2,50], is the number of folds to use for cross-validation.

  • "TestDataFraction"，在[0,0.5]范围内指定为数字标量，是保留用于测试的数据的一部分。

回归线（文件名）opens the Regression Learner app with the previously saved session infilename。这filenameargument, specified as a character vector or string scalar, must include the name of a Regression Learner session file and the path to the file, if it is not in the current folder. The file must have the extension。mat。

限制

  • 回归学习者does not support model deployment toMATLAB Production Server™inMATLAB Online™。

Version History

Introduced in R2017a

See Also

Apps

Functions