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.
regressionLearner opens the Regression Learner app or brings
focus to the app if it is already open.
regressionLearner(Tbl,ResponseVarName)
regressionLearner(Tbl,ResponseVarName) opens the Regression
Learner app and populates the New Session from Arguments dialog box with the data
contained in the table Tbl. The
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
predictor variables.
regressionLearner(Tbl,Y)
regressionLearner(Tbl,Y) opens the Regression Learner app and
populates the New Session from Arguments dialog box with the predictor variables in
the table Tbl and the response values in the numeric vector
Y.
regressionLearner(X,Y)
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
equal.
regressionLearner(___,Name,Value)
regressionLearner(___,Name,Value) 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',10 to use a 10-fold
cross-validation scheme.
'CrossVal', specified as 'on'
(default) or 'off', is the cross-validation flag. If you
specify 'on', then the app uses 5-fold cross-validation.
If you specify 'off', then the app uses resubstitution
validation.
You can override the 'CrossVal' cross-validation
setting by using the 'Holdout' or
'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.
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