CompactRegressionSVM.predict - Predict responses using support vector machine regression model - MATLAB (original) (raw)

Class: classreg.learning.regr.CompactRegressionSVM, RegressionSVM
Namespace: classreg.learning.regr

Predict responses using support vector machine regression model

Syntax

Description

[yfit](#buvlclx-yfit) = predict([Mdl](#buvlclx-Mdl),[X](#buvlclx-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.

example

[yfit](#buvlclx-yfit) = predict([Mdl](#buvlclx-Mdl),[X](#buvlclx-X),PredictionForMissingValue=[prediction](#buvlclx%5Fsep%5Fmw%5F0a5f3ed1-774b-425e-a442-92f5a43fb279)) uses the prediction value as the predicted response for observations with missing values in the predictor data X. By default, predict uses the median of the observed response values in the training data. (since R2023b)

Input Arguments

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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 ofX must have the same order as the predictor variables that trainedMdl.
  • If you trained Mdl using a table (for example, Tbl), thenX can be a numeric matrix ifTbl contains all numeric predictor variables. To treat numeric predictors inTbl as categorical during training, identify categorical predictors using theCategoricalPredictors name-value pair argument of fitrsvm. IfTbl 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 inMdl.PredictorNames). However, the column order of X does not need to correspond to the column order ofTbl. Tbl andX 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 inMdl.PredictorNames and corresponding predictor variable names inX 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 infitrsvm to train Mdl, then the software standardizes the columns of X using the corresponding means in Mdl.Mu and standard deviations inMdl.Sigma.

Data Types: table | double | single

Since R2023b

Predicted response value to use for observations with missing predictor values, specified as "median", "mean", or a numeric scalar.

Example: "mean"

Example: NaN

Data Types: single | double | char | string

Output Arguments

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

• If mdl is a cross-validated RegressionPartitionedSVM model, usekfoldPredict 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

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

Usage notes and limitations:

• You can generate C/C++ code for both predict andupdate by using a coder configurer. Or, generate code only forpredict 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 thepredict 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 forpredict, specify DataType="single" when you call the loadLearnerForCoder function.

• You can also generate fixed-point C/C++ code forpredict. 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 then use the structure as an input argument ofloadLearnerForCoder 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).
  Name-value arguments	Names in name-value arguments must be compile-time constants.If the value of PredictionForMissingValue is nonnumeric, then it must be a compile-time constant.

For more information, see Introduction to Code Generation.

Version History

Introduced in R2015b

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Starting in R2023b, when you predict or compute the loss, some regression models allow you to specify the predicted response value for observations with missing predictor values. Specify the PredictionForMissingValue name-value argument to use a numeric scalar, the training set median, or the training set mean as the predicted value. When computing the loss, you can also specify to omit observations with missing predictor values.

This table lists the object functions that support thePredictionForMissingValue name-value argument. By default, the functions use the training set median as the predicted response value for observations with missing predictor values.

In previous releases, the regression model loss and predict functions listed above used NaN predicted response values for observations with missing predictor values. The software omitted observations with missing predictor values from the resubstitution ("resub") and cross-validation ("kfold") computations for prediction and loss.

Starting in R2023a, predict fully supports GPU arrays.