Predict responses using support vector machine regression model
Mdl — SVM regression modelRegressionSVM object | CompactRegressionSVM objectSVM regression model, specified as a RegressionSVM model or a CompactRegressionSVM model,
returned by fitrsvm or compact,
respectively.
X — Predictor data used to generate responsesPredictor data used to generate responses, specified as a numeric matrix or table.
Each row of X corresponds to one observation,
and each column corresponds to one variable.
For a numeric matrix:
The variables making up the columns of X must
have the same order as the predictor variables that trained Mdl.
If you trained Mdl using a table (for example, Tbl),
then X can be a numeric matrix if
Tbl contains all numeric
predictor variables. To treat numeric predictors in
Tbl as categorical during
training, identify categorical predictors using the
CategoricalPredictors name-value pair
argument of fitrsvm. If
Tbl contains heterogeneous
predictor variables (for example, numeric and
categorical data types) and X is a
numeric matrix, then predict throws
an error.
For a table:
predict does not support multi-column
variables and cell arrays other than cell arrays of character vectors.
If you trained Mdl using a table
(for example, Tbl), then all predictor variables
in X must have the same variable names and data
types as those that trained Mdl (stored in Mdl.PredictorNames).
However, the column order of X does not need to
correspond to the column order of Tbl. Tbl and X can
contain additional variables (response variables, observation weights,
etc.), but predict ignores them.
If you trained Mdl using a numeric matrix, then the predictor names in
Mdl.PredictorNames and
corresponding predictor variable names in
X must be the same. To specify
predictor names during training, see the PredictorNames name-value pair argument
of fitrsvm. All predictor variables
in X must be numeric vectors.
X can contain additional
variables (response variables, observation weights,
etc.), but predict ignores
them.
If you set 'Standardize',true in fitrsvm to
train Mdl, then the software standardizes the columns
of X using the corresponding means in Mdl.Mu and
standard deviations in Mdl.Sigma.
Data Types: table | double | single
yfit — Predicted responsesPredicted responses, returned as a vector of length n, where n is the number of observations in the training data.
For details about how to predict responses, see Equation 1 and Equation 2 in Understanding Support Vector Machine Regression.
Load the carsmall data set. Consider a model that predicts a car's fuel efficiency given its horsepower and weight. Determine the sample size.
load carsmall
tbl = table(Horsepower,Weight,MPG);
N = size(tbl,1);Partition the data into training and test sets. Hold out 10% of the data for testing.
rng(10); % For reproducibility cvp = cvpartition(N,'Holdout',0.1); idxTrn = training(cvp); % Training set indices idxTest = test(cvp); % Test set indices
Train a linear SVM regression model. Standardize the data.
Mdl = fitrsvm(tbl(idxTrn,:),'MPG','Standardize',true);
Mdl is a RegressionSVM model.
Predict responses for the test set.
YFit = predict(Mdl,tbl(idxTest,:));
Create a table containing the observed response values and the predicted response values side by side.
table(tbl.MPG(idxTest),YFit,'VariableNames',... {'ObservedValue','PredictedValue'})
ans=10×2 table
ObservedValue PredictedValue
_____________ ______________
14 9.4833
27 28.938
10 7.765
28 27.155
22 21.054
29 31.484
24.5 30.306
18.5 19.12
32 28.225
28 26.632
If mdl is a cross-validated RegressionPartitionedSVM model, use
kfoldPredict instead of predict to predict new response values.
This function fully supports tall arrays. You can use models trained on either in-memory or tall data with this function.
For more information, see Tall Arrays (MATLAB).
Usage notes and limitations:
You can generate C/C++ code for both predict and
update by using a coder configurer. Or, generate code only for
predict by using saveLearnerForCoder,
loadLearnerForCoder, and codegen.
Code generation for predict and update — Create a coder configurer by using learnerCoderConfigurer and then generate code by using generateCode. Then you can update model parameters in the
generated code without having to regenerate the code.
Code generation for predict — Save a trained model by
using saveLearnerForCoder. Define an
entry-point function that loads the saved model by using loadLearnerForCoder and calls the
predict function. Then use codegen to generate code for the
entry-point function.
You can also generate fixed-point C/C++ code for
predict. Fixed-point code generation requires an additional step that
defines the fixed-point data types of the variables required for prediction. Create a
fixed-point data type structure by using the data type function
generated by generateLearnerDataTypeFcn, and use the structure as an input argument of
loadLearnerForCoder in an entry-point function. Generating fixed-point
C/C++ code requires MATLAB®
Coder™ and Fixed-Point
Designer™.
This table contains
notes about the arguments of predict. Arguments not included in this
table are fully supported.
| Argument | Notes and Limitations |
|---|---|
Mdl | For the usage notes and limitations of the model object,
see
Code Generation of the |
X |
|
For more information, see Introduction to Code Generation.
CompactRegressionSVM | RegressionSVM | fitrsvm | kfoldPredict
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