www.gusucode.com > stats 源码程序 matlab案例代码 > stats/FindGoodLassoPenaltyUsingkfoldEdge1Example.m
%% Find Good Lasso Penalty Using _k_-fold Edge % To determine a good lasso-penalty strength for a linear classification % model that uses a logistic regression learner, compare k-fold edges. %% % Load the NLP data set. Preprocess the data as in % <docid:stats_ug.bu6xz2d-1>. load nlpdata Y(~(ismember(Y,{'simulink','dsp','comm'}))) = 'others'; X = X'; %% % Create a set of 8 logarithmically-spaced regularization strengths from % $10^{-8}$ through $10^{1}$. Lambda = logspace(-8,1,8); %% % Create a linear classification model template that specifies to use % logistic regression with a lasso penalty, use each of the regularization % strengths, solve the objective function using SpaRSA, and reduce the % tolerance on the gradient of the objective function to |1e-8|. t = templateLinear('Learner','logistic','Solver','sparsa',... 'Regularization','lasso','Lambda',Lambda,'GradientTolerance',1e-8); %% % Cross-validate an ECOC model composed of binary, linear classification % models using 5-fold cross-validation and that rng(10); % For reproducibility CVMdl = fitcecoc(X,Y,'Learners',t,'ObservationsIn','columns','KFold',5) %% % |CVMdl| is a |ClassificationPartitionedLinearECOC| model. %% % Estimate the edges for each fold and regularization strength. eFolds = kfoldEdge(CVMdl,'Mode','individual') %% % |eFolds| is a 5-by-8 matrix of edges. Rows correspond to folds and % columns correspond to regularization strengths in |Lambda|. You can use % |eFolds| to identify ill-performing folds, that is, unusually low edges. %% % Estimate the average edge over all folds for each regularization % strength. e = kfoldEdge(CVMdl) %% % Determine how well the models generalize by plotting the averages of the % 5-fold edge for each regularization strength. Identify the % regularization strength that maximizes the 5-fold edge over the grid. figure; plot(log10(Lambda),log10(e),'-o') [~, maxEIdx] = max(e); maxLambda = Lambda(maxEIdx); hold on plot(log10(maxLambda),log10(e(maxEIdx)),'ro'); ylabel('log_{10} 5-fold edge') xlabel('log_{10} Lambda') legend('Edge','Max edge') hold off %% % Several values of |Lambda| yield similarly high edges. Greater % regularization strength values lead to predictor variable sparsity, which % is a good quality of a classifier. %% % Choose the regularization strength that occurs just before % the edge starts decreasing. LambdaFinal = Lambda(4); %% % Train an ECOC model composed of linear classification model using the % entire data set and specify the regularization strength |LambdaFinal|. t = templateLinear('Learner','logistic','Solver','sparsa',... 'Regularization','lasso','Lambda',LambdaFinal,'GradientTolerance',1e-8); MdlFinal = fitcecoc(X,Y,'Learners',t,'ObservationsIn','columns'); %% % To estimate labels for new observations, pass |MdlFinal| and the new data % to |predict|.