www.gusucode.com > stats 源码程序 matlab案例代码 > stats/CreateCrossValidatedLinearClassificationModelExample.m
%% Create Cross-Validated Binary Linear Classification Model %% % Load the NLP data set. load nlpdata %% % |X| is a sparse matrix of predictor data, and |Y| is a categorical vector % of class labels. There are more than two classes in the data. %% % Identify the labels that correspond to the Statistics and Machine % Learning Toolbox(TM) documentation web pages. Ystats = Y == 'stats'; %% % Cross-validate a binary, linear classification model that can identify whether % the word counts in a documentation web page are from the Statistics and % Machine Learning Toolbox(TM) documentation. rng(1); % For reproducibility CVMdl = fitclinear(X,Ystats,'CrossVal','on') %% % |CVMdl| is a |ClassificationPartitionedLinear| cross-validated model. % Because |fitclinear| implements 10-fold cross-validation by default, % |CVMdl.Trained| contains ten |ClassificationLinear| models that contain % the results of training linear classification models for each of the folds. %% % Estimate labels for out-of-fold observations and estimate the % generalization error by passing |CVMdl| to |kfoldPredict| and % |kfoldLoss|, respectively. oofLabels = kfoldPredict(CVMdl); ge = kfoldLoss(CVMdl) %% % The estimated generalization error is less than 0.1% misclassified % observations.