www.gusucode.com > stats 源码程序 matlab案例代码 > stats/OptimizeALinearClassifierExample.m
%% Optimize Linear Classifier % This example shows how to minimize the cross-validation error in a linear % classifier using |fitclinear|. The example uses the NLP data set. %% % 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. % % The models should identify whether the word counts in a web page are from % the Statistics and Machine Learning Toolbox™ documentation. % Identify the relevant labels. X = X'; Ystats = Y == 'stats'; %% % Optimize the classification using the |'auto'| parameters. % % For reproducibility, set the random seed and use the % |'expected-improvement-plus'| acquisition function. rng default Mdl = fitclinear(X,Ystats,'ObservationsIn','columns','Solver','sparsa',... 'OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',... struct('AcquisitionFunctionName','expected-improvement-plus'))