kfoldPredict

Predict responses for observations in cross-validated regression model

Syntax

yfit = kfoldPredict(CVMdl)

yfit = kfoldPredict(CVMdl,'IncludeInteractions',includeInteractions)

Description

yfit = kfoldPredict(CVMdl) returns responses predicted by the cross-validated regression model CVMdl. For every fold, kfoldPredict predicts the responses for validation-fold observations using a model trained on training-fold observations. CVMdl.X and CVMdl.Y contain both sets of observations.

yfit = kfoldPredict(CVMdl,'IncludeInteractions',includeInteractions) specifies whether to include interaction terms in computations. This syntax applies only to generalized additive models.

Examples

Compute Cross-Validation Loss Manually

When you create a cross-validated regression model, you can compute the mean squared error (MSE) by using the kfoldLoss object function. Alternatively, you can predict responses for validation-fold observations using kfoldPredict and compute the MSE manually.

Load the carsmall data set. Specify the predictor data X and the response data Y.

load carsmall
X = [Cylinders Displacement Horsepower Weight];
Y = MPG;

Train a cross-validated regression tree model. By default, the software implements 10-fold cross-validation.

rng('default') % For reproducibility
CVMdl = fitrtree(X,Y,'CrossVal','on');

Compute the 10-fold cross-validation MSE by using kfoldLoss.

L = kfoldLoss(CVMdl)

L = 29.4963

Predict the responses yfit by using the cross-validated regression model. Compute the mean squared error between yfit and the true responses CVMdl.Y. The computed MSE matches the loss value returned by kfoldLoss.

yfit = kfoldPredict(CVMdl);
mse = mean((yfit - CVMdl.Y).^2)

mse = 29.4963

Input Arguments

CVMdl — Cross-validated partitioned regression model

RegressionPartitionedModel object | RegressionPartitionedEnsemble object | RegressionPartitionedGAM object | RegressionPartitionedSVM object

Cross-validated partitioned regression model, specified as a RegressionPartitionedModel, RegressionPartitionedEnsemble, RegressionPartitionedGAM, or RegressionPartitionedSVM object. You can create the in two ways:

• Pass a trained regression model listed in the following table to its crossval object function.

• Train a regression model using a function listed in the following table and specify one of the cross-validation name-value arguments for the function.

Regression Model	Function
RegressionEnsemble	fitrensemble
RegressionGAM	fitrgam
RegressionGP	fitrgp
RegressionNeuralNetwork	fitrnet
RegressionSVM	fitrsvm
RegressionTree	fitrtree

includeInteractions — Flag to include interaction terms

true | false

Flag to include interaction terms of the model, specified as true or false. This argument is valid only for a generalized additive model (GAM). That is, you can specify this argument only when CVMdl is RegressionPartitionedGAM.

The default value is true if the models in CVMdl (CVMdl.Trained) contain interaction terms. The value must be false if the models do not contain interaction terms.

Data Types: logical

Output Arguments

yfit — Predicted responses

numeric vector

Predicted responses, returned as an n-by-1 numeric vector, where n is the number of observations. (n is size(CVMdl.X,1) when observations are in rows.) Each entry of yfit corresponds to the predicted response for the corresponding row of CVMdl.X.

If you use a holdout validation technique to create CVMdl (that is, if CVMdl.KFold is 1), then yfit has NaN values for training-fold observations.