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    %% Estimate Test-Sample Mean Squared Error
%%
% Simulate 10000 observations from this model
%
% $$y = x_{100} + 2x_{200} + e.$$
%
%
% * $X = {x_1,...,x_{1000}}$ is a 10000-by-1000 sparse matrix with 10%
% nonzero standard normal elements.
% * _e_ is random normal error with mean 0 and standard deviation
% 0.3.
%
rng(1) % For reproducibility
n = 1e4;
d = 1e3;
nz = 0.1;
X = sprandn(n,d,nz);
Y = X(:,100) + 2*X(:,200) + 0.3*randn(n,1);
%%
% Train a linear regression model.  Reserve 30% of the observations as a
% holdout sample.
CVMdl = fitrlinear(X,Y,'Holdout',0.3);
Mdl = CVMdl.Trained{1}
%%
% |CVMdl| is a |RegressionPartitionedLinear| model. It contains
% the property |Trained|, which is a 1-by-1 cell array holding a
% |RegressionLinear| model that the software trained using the
% training set.
%%
% Extract the training and test data from the partition definition.
trainIdx = training(CVMdl.Partition);
testIdx = test(CVMdl.Partition);
%%
% Estimate the training- and test-sample MSE.
mseTrain = loss(Mdl,X(trainIdx,:),Y(trainIdx))
mseTest = loss(Mdl,X(testIdx,:),Y(testIdx))
%%
% Because there is one regularization strength in |Mdl|, |mseTrain| and
% |mseTest| are numeric scalars.