www.gusucode.com > stats 源码程序 matlab案例代码 > stats/PredictClassLabelsUsingDiscriminantAnalysisModelExample.m
%% Predict Class Labels Using Discriminant Analysis Model %% % Load Fisher's iris data set. Determine the sample size. load fisheriris N = size(meas,1); %% % Partition the data into training and test sets. Hold out 10% of the data % for testing. rng(1); % For reproducibility cvp = cvpartition(N,'Holdout',0.1); idxTrn = training(cvp); % Training set indices idxTest = test(cvp); % Test set indices %% % Store the training data in a table. tblTrn = array2table(meas(idxTrn,:)); tblTrn.Y = species(idxTrn); %% % Train a discriminant analysis model using the training set and default % options. Mdl = fitcdiscr(tblTrn,'Y'); %% % Predict labels for the test set. You trained |Mdl| using a table of data, % but you can predict labels using a matrix. labels = predict(Mdl,meas(idxTest,:)); %% % Construct a confusion matrix for the test set. Mdl.ClassNames confusionmat(species(idxTest),labels) %% % |Mdl| misclassifies one versicolor iris as virginica in the test set.