www.gusucode.com > stats 源码程序 matlab案例代码 > stats/PredictCrossValidatedResponsesExample.m
%% Predict Cross-Validated Responses %% % 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); %% % Cross-validate a linear regression model. CVMdl = fitrlinear(X,Y,'CrossVal','on') Mdl1 = CVMdl.Trained{1} %% % By default, |fitrlinear| implements 10-fold cross-validation. |CVMdl| is % a |RegressionPartitionedLinear| model. It contains the property % |Trained|, which is a 10-by-1 cell array holding 10 |RegressionLinear| % models that the software trained using the training set. %% % Predict responses for observations that |fitrlinear| did not use in % training the folds. yHat = kfoldPredict(CVMdl); %% % Because there is one regularization strength in |Mdl|, |yHat| is a % numeric vector.