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

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`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 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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`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

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Predict Test Sample Response for SVM Regression Model

Open Live Script

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. You can use models trained on either in-memory or tall data with this function.

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.

You can also generate single-precision C/C++ code for predict. For single-precision code generation, specify the name-value pair argument 'DataType','single' as an additional input to 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

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 `single` or `double` predictor variables. If you want to specify `X` as a table, then your model must be trained using a table, and you must ensure that your entry-point function for prediction: Accepts data as arrays Creates a table from the data input arguments and specifies the variable names in the table Passes the table to `predict` For an example of this table workflow, see Generate Code to Classify Numeric 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). In the coder configurer workflow, `X` must be a `single` or `double` 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.

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 single or double predictor variables.
If you want to specify X as a table, then your model must be trained using a table, and you must ensure that your entry-point function for prediction:
- Accepts data as arrays
- Creates a table from the data input arguments and specifies the variable names in the table
- Passes the table to predict
For an example of this table workflow, see Generate Code to Classify Numeric 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).
In the coder configurer workflow, X must be a single or double 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.

For more information, see Introduction to Code Generation.

Documentation

predict

Syntax

Description

Input Arguments

`Mdl` — SVM regression model
`RegressionSVM` object | `CompactRegressionSVM` object

`X` — Predictor data used to generate responses
numeric matrix | table

Output Arguments

`yfit` — Predicted responses
vector

Examples

Predict Test Sample Response for SVM Regression Model

Tips

Alternative Functionality

Simulink Block

Extended Capabilities

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

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

See Also

Statistics and Machine Learning Toolbox Documentation

Support

Documentation

predict

Syntax

Description

Input Arguments

Mdl — SVM regression model RegressionSVM object | CompactRegressionSVM object

X — Predictor data used to generate responses numeric matrix | table

Output Arguments

yfit — Predicted responses vector

Examples

Predict Test Sample Response for SVM Regression Model

Tips

Alternative Functionality

Simulink Block

Extended Capabilities

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

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

See Also

Statistics and Machine Learning Toolbox Documentation

Support

`Mdl` — SVM regression model
`RegressionSVM` object | `CompactRegressionSVM` object

`X` — Predictor data used to generate responses
numeric matrix | table

`yfit` — Predicted responses
vector

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

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.