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predict

Predict responses for Gaussian kernel regression model

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Syntax

YFit = predict(Mdl,X)
YFit = predict(Mdl,X,PredictionForMissingValue=prediction)

Description

example

YFit = predict(Mdl,X) returns a vector of predicted responses for the predictor data in the matrix or table X, based on the binary Gaussian kernel regression model Mdl.

YFit = predict(Mdl,X,PredictionForMissingValue=prediction) 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.

load carbig

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

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)
L = 9.2664

Input Arguments

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Kernel regression model, specified as a RegressionKernel model object. You can create a RegressionKernel 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) and Tbl contains all numeric predictor variables, then X can be a numeric matrix. To treat numeric predictors in Tbl as categorical during training, identify categorical predictors using the CategoricalPredictors name-value pair argument of fitrkernel. 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. Also, Tbl and X 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 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 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.

ValueDescription
"median"predict uses the median of the observed response values in the training data as the predicted response value for observations with missing predictor values.
"mean"predict uses the mean of the observed response values in the training data as the predicted response value for observations with missing predictor values.
Numeric scalarpredict uses this value as the predicted response value for observations with missing predictor values.

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 train Mdl, where n is the number of observations in X.

Extended Capabilities

Version History

Introduced in R2018a

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See Also

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