This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.


Predict responses using support vector machine regression model


yfit = predict(Mdl,X)



yfit = predict(Mdl,X) returns a vector of predicted responses for the predictor data in the table or matrix X, based on the full or compact, trained support vector machine (SVM) regression model Mdl.

Input Arguments

expand all

SVM regression model, specified as a RegressionSVM model or a CompactRegressionSVM model, returned by fitrsvm or compact, respectively.

Predictor 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

Output Arguments

expand all

Predicted 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.


expand all

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.

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    


Extended Capabilities

Introduced in R2015b