predict - Predict responses for Gaussian kernel regression model - MATLAB (original) (raw)

Predict responses for Gaussian kernel regression model

Syntax

Description

[YFit](#d126e1290877) = predict([Mdl](#d126e1290658),[X](#mw%5F64962b9c-8bb5-4ba8-9648-9851f8a2c806)) returns a vector of predicted responses for the predictor data in the matrix or table X, based on the binary Gaussian kernel regression modelMdl.

example

[YFit](#d126e1290877) = predict([Mdl](#d126e1290658),[X](#mw%5F64962b9c-8bb5-4ba8-9648-9851f8a2c806),PredictionForMissingValue=[prediction](#mw%5F69ef01f6-4f5b-4cc8-a337-7405c201ec6a%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)

Examples

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Predict the test set responses using a Gaussian kernel regression model for the carbig data set.

Load the carbig data set.

Specify the predictor variables (X) and the response variable (Y).

X = [Weight,Cylinders,Horsepower,Model_Year]; Y = MPG;

Delete rows of X and Y where either array has NaN values. Removing rows with NaN values before passing data to fitrkernel can speed up training and reduce memory usage.

R = rmmissing([X Y]); X = R(:,1:4); Y = R(:,end);

Reserve 10% of the observations as a holdout sample. Extract the training and test indices from the partition definition.

rng(10) % For reproducibility N = length(Y); cvp = cvpartition(N,'Holdout',0.1); idxTrn = training(cvp); % Training set indices idxTest = test(cvp); % Test set indices

Train the regression kernel model. Standardize the training data.

Xtrain = X(idxTrn,:); Ytrain = Y(idxTrn); Mdl = fitrkernel(Xtrain,Ytrain,'Standardize',true)

Mdl = RegressionKernel ResponseName: 'Y' Learner: 'svm' NumExpansionDimensions: 128 KernelScale: 1 Lambda: 0.0028 BoxConstraint: 1 Epsilon: 0.8617

Properties, Methods

Mdl is a RegressionKernel model.

Predict responses for the test set.

Xtest = X(idxTest,:); Ytest = Y(idxTest);

YFit = predict(Mdl,Xtest);

Create a table containing the first 10 observed response values and predicted response values.

table(Ytest(1:10),YFit(1:10),'VariableNames', ... {'ObservedValue','PredictedValue'})

ans=10×2 table ObservedValue PredictedValue _____________ ______________

     18              17.616    
     14              25.799    
     24              24.141    
     25              25.018    
     14              13.637    
     14              14.557    
     18              18.584    
     27              26.096    
     21              25.031    
     13              13.324    

Estimate the test set regression loss using the mean squared error loss function.

L = loss(Mdl,Xtest,Ytest)

Input Arguments

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Kernel regression model, specified as a RegressionKernel model object. You can create aRegressionKernel model object using fitrkernel.

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 in 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) andTbl contains all numeric predictor variables, then X can be a numeric matrix. To treat numeric predictors inTbl as categorical during training, identify categorical predictors using theCategoricalPredictors name-value pair argument of fitrkernel. If Tbl contains heterogeneous predictor variables (for example, numeric and categorical data types) andX is a numeric matrix, thenpredict 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. Also, Tbl andX can contain additional variables (response variables, observation weights, and so on), 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 fitrkernel. All predictor variables in X must be numeric vectors. X can contain additional variables (response variables, observation weights, and so on), but predict ignores them.

Data Types: double | single | table

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

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Predicted responses, returned as a numeric vector.

YFit is an_n_-by-1 vector of the same data type as the response data (Y) used to trainMdl, where n is the number of observations in X.

Extended Capabilities

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Thepredict function supports tall arrays with the following usage notes and limitations:

• predict does not support tall table data.

For more information, see Tall Arrays.

Usage notes and limitations:

• Use saveLearnerForCoder, loadLearnerForCoder, and codegen (MATLAB Coder) to generate code for the predict function. 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 to generate code for the entry-point function.
• To generate single-precision C/C++ code forpredict, specify DataType="single" when you call the loadLearnerForCoder function.
• If the code generator uses the Open Multiprocessing (OpenMP) library, the generated code of predict splits the predictor dataX into multiple chunks and predicts responses for the chunks in parallel. The generated code uses parfor (MATLAB Coder) to create loops that run in parallel on supported shared-memory multicore platforms. If your compiler does not support the OpenMP application interface, or if you disable the OpenMP library, the generated code does not split the predictor data and, therefore, processes one observation at a time. To find supported compilers, see Supported Compilers. To disable the OpenMP library, set the EnableOpenMP property of the configuration object to false. For details, see coder.CodeConfig (MATLAB Coder).
• 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 RegressionKernel 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.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 R2018a

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predict fully supports GPU arrays.

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.

You can generate C/C++ code for the predict function.