Train regression models to predict data using supervised machine learning
The Regression Learner 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, ensembles of regression trees, and neural network regression models.
Perform supervised machine learning by supplying a known set of observations of input data (predictors) and known responses. Use the observations to train a model that generates predicted responses for new input data. To use the model with new data, or to learn about programmatic regression, you can export the model to the workspace or generate MATLAB® code to recreate the trained model.
Statistics and Machine Learning Toolbox™
Note: When you use Regression Learner in MATLAB Online™, you can train models in parallel using a Cloud Center cluster (requires Parallel Computing Toolbox™). For more information, see Use Parallel Computing Toolbox with Cloud Center Cluster in MATLAB Online (Parallel Computing Toolbox).
MATLAB Toolstrip: On the Apps tab, under Machine Learning, click the app icon.
MATLAB command prompt: Enter
regressionLearner opens the Regression Learner app or brings
focus to the app if it is already open.
regressionLearner(Tbl,ResponseVarName) opens the Regression
Learner app and populates the New Session from Arguments dialog box with the data
contained in the table
ResponseVarName argument, specified as a character vector or
string scalar, is the name of the variable in
Tbl that contains
the response values. The remaining variables in
Tbl are the
regressionLearner(Tbl,Y) opens the Regression Learner app and
populates the New Session from Arguments dialog box with the predictor variables in
Tbl and the response values in the numeric vector
regressionLearner(X,Y) opens the Regression Learner app and
populates the New Session from Arguments dialog box with the
n-by-p predictor matrix
X and the n response values in the vector
Y. Each row of
X corresponds to one
observation, and each column corresponds to one variable. The length of
Y and the number of rows of
X must be
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',10 to use a 10-fold
'CrossVal', specified as
'off', is the cross-validation flag. If you
'on', then the app uses 5-fold cross-validation.
If you specify
'off', then the app uses resubstitution
You can override the
setting by using the
'KFold' name-value argument. You can specify only one
of these arguments at a time.
'Holdout', specified as a numeric scalar in the range
[0.05,0.5], is the fraction of the data used for holdout validation. The app
uses the remaining data for training.
'KFold', specified as a positive integer in the range
[2,50], is the number of folds to use for cross-validation.