kfoldPredict

Predict responses for observations in cross-validated kernel regression model

Syntax

YHat = kfoldPredict(CVMdl)

YHat = kfoldPredict(CVMdl,PredictionForMissingValue=prediction)

Description

YHat = kfoldPredict(CVMdl) returns cross-validated predicted responses by the cross-validated kernel regression model CVMdl. For every fold, kfoldPredict predicts the responses for validation-fold observations using a model trained on training-fold observations.

YHat = kfoldPredict(CVMdl,PredictionForMissingValue=prediction) uses the prediction value as the predicted response for observations with missing values in the predictor data. By default, kfoldPredict uses the median of the observed response values in the training-fold data. (since R2023b)

Examples

Predict Responses from Cross-Validated Kernel Regression Models

Simulate sample data:

rng(0,'twister'); % For reproducibility
n = 1000;
x = linspace(-10,10,n)';
y = 1 + x*2e-2 + sin(x)./x + 0.2*randn(n,1);

Cross-validate a kernel regression model.

CVMdl = fitrkernel(x,y,'CrossVal','on');

By default, fitrkernel implements 10-fold cross-validation. CVMdl is a RegressionPartitionedKernel model. It contains the property Trained, which is a 10-by-1 cell array holding 10 RegressionKernel models that the software trained using the training set.

Predict responses for observations that fitrkernel did not use in training the folds.

yHat = kfoldPredict(CVMdl);

yHat is a numeric vector. Display the first five predicted responses.

yHat(1:5)

ans = 5×1

    0.8869
    0.7744
    0.8915
    0.8040
    0.8870

Input Arguments

CVMdl — Cross-validated kernel regression model

RegressionPartitionedKernel model object

Cross-validated kernel regression model, specified as a RegressionPartitionedKernel model object. You can create a RegressionPartitionedKernel model using fitrkernel and specifying any of the one of the cross-validation name-value pair arguments, for example, CrossVal.

To obtain estimates, kfoldPredict applies the same data used to cross-validate the kernel regression model (see X input argument on fitrkernel page).

prediction — Predicted response value to use for observations with missing predictor values

"median" (default) | "mean" | numeric scalar

Since R2023b

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

Value	Description
"median"	kfoldPredict uses the median of the observed response values in the training-fold data as the predicted response value for observations with missing predictor values.
"mean"	kfoldPredict uses the mean of the observed response values in the training-fold data as the predicted response value for observations with missing predictor values.
Numeric scalar	kfoldPredict 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

YHat — Cross-validated predicted responses

numeric vector

Cross-validated predicted responses, returned as an n-by-1 numeric array, where n is the number of observations in the predictor data used to create CVMdl (see X input argument on fitrkernel page).

Version History

Introduced in R2018b

R2023b: Specify predicted response value to use for observations with missing predictor values

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 the PredictionForMissingValue name-value argument. By default, the functions use the training set median as the predicted response value for observations with missing predictor values.

Model Type	Model Objects	Object Functions
Gaussian process regression (GPR) model	RegressionGP, CompactRegressionGP	loss, predict, resubLoss, resubPredict
	RegressionPartitionedGP	kfoldLoss, kfoldPredict
Gaussian kernel regression model	RegressionKernel	loss, predict
	RegressionPartitionedKernel	kfoldLoss, kfoldPredict
Linear regression model	RegressionLinear	loss, predict
	RegressionPartitionedLinear	kfoldLoss, kfoldPredict
Neural network regression model	RegressionNeuralNetwork, CompactRegressionNeuralNetwork	loss, predict, resubLoss, resubPredict
	RegressionPartitionedNeuralNetwork	kfoldLoss, kfoldPredict
Support vector machine (SVM) regression model	RegressionSVM, CompactRegressionSVM	loss, predict, resubLoss, resubPredict
	RegressionPartitionedSVM	kfoldLoss, kfoldPredict

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.