predict

Predict responses using support vector machine regression model

Syntax

yfit = predict(Mdl,X)

Description

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

Mdl — SVM regression model

RegressionSVM object | CompactRegressionSVM object

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

X — Predictor data used to generate responses

numeric matrix | table

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 multicolumn variables or 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

yfit — Predicted responses

vector

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

Predict Test Sample Response for SVM Regression Model

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

• If mdl is a cross-validated RegressionPartitionedSVM model, use kfoldPredict instead of predict to predict new response values.

Alternative Functionality

Simulink Block

To integrate the prediction of an SVM regression model into Simulink^®, you can use the RegressionSVM Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^® Function block with the predict function. For examples, see Predict Responses Using RegressionSVM Predict Block and Predict Class Labels Using MATLAB Function Block.

When deciding which approach to use, consider the following:

• If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.

• Support for variable-size arrays must be enabled for a MATLAB Function block with the predict function.

• If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. For more information, see Tall Arrays.

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

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 (MATLAB Coder) to generate code for the entry-point function.

• For single-precision code generation, use standardized data by specifying 'Standardize',true when you train the model. To generate single-precision C/C++ code for predict, specify the name-value argument "DataType","single" when you call the loadLearnerForCoder 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 CompactRegressionSVM object.
  X	For general code generation, X must be a single-precision or double-precision matrix or a table containing numeric variables, categorical variables, or both. In the coder configurer workflow, X must be a single-precision or double-precision matrix. For fixed-point code generation, X must be a fixed-point matrix. The number of rows, or observations, in X can be a variable size, but the number of columns in X must be fixed. If you want to specify X as a table, then your model must be trained using a table, and your entry-point function for prediction must do the following: Accept data as arrays. Create a table from the data input arguments and specify the variable names in the table. Pass the table to predict. For an example of this table workflow, see Generate Code to Classify Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder).

For more information, see Introduction to Code Generation.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2015b

R2023a: GPU array support

Starting in R2023a, predict fully supports GPU arrays.