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predict

Predict responses using support vector machine regression model

Syntax

yfit = predict(Mdl,X)

Description

example

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

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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

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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.

Examples

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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    

Tips

Extended Capabilities

Introduced in R2015b