www.gusucode.com > stats 源码程序 matlab案例代码 > stats/FindGoodLassoPenaltyUsingKfoldEdgeExample.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.bu6xx1d>. load nlpdata Ystats = Y == 'stats'; X = X'; %% % Create a set of 11 logarithmically-spaced regularization strengths from % $10^{-8}$ through $10^{1}$. Lambda = logspace(-8,1,11); %% % Cross-validate a binary, linear classification model using 5-fold % cross-validation and that uses each of the regularization strengths. % Solve the objective function using SpaRSA. Lower the tolerance on the % gradient of the objective function to |1e-8|. % rng(10); % For reproducibility CVMdl = fitclinear(X,Ystats,'ObservationsIn','columns','KFold',5,... 'Learner','logistic','Solver','sparsa','Regularization','lasso',... 'Lambda',Lambda,'GradientTolerance',1e-8) %% % |CVMdl| is a |ClassificationPartitionedLinear| model. Because |fitclinear| % implements 5-fold cross-validation, |CVMdl| contains 5 % |ClassificationLinear| models that the software trains on each fold. %% % Estimate the edges for each fold and regularization strength. eFolds = kfoldEdge(CVMdl,'Mode','individual') %% % |eFolds| is a 5-by-11 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. Higher values of % lambda 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(5); %% % Train a linear classification model using the entire data set and specify % the regularization strength |LambdaFinal|. MdlFinal = fitclinear(X,Ystats,'ObservationsIn','columns',... 'Learner','logistic','Solver','sparsa','Regularization','lasso',... 'Lambda',LambdaFinal); %% % To estimate labels for new observations, pass |MdlFinal| and the new data % to |predict|.