www.gusucode.com > stats 源码程序 matlab案例代码 > stats/EstimateSigmoidTransformationFunctionFromTrainedSVMModelExample.m
%% Fit the Score-to-Posterior Probability Function for Inseparable Classes %% % Load the |ionosphere| data set. % Copyright 2015 The MathWorks, Inc. load ionosphere %% % The classes of this data set are not separable. %% % Train an SVM classifier. Cross validate using 10-fold cross validation % (the default). It is good practice to standardize the predictors and % specify the class order. rng(1) % For reproducibility CVSVMModel = fitcsvm(X,Y,'ClassNames',{'b','g'},'Standardize',true,... 'CrossVal','on'); ScoreTransform = CVSVMModel.ScoreTransform %% % |CVSVMModel| is a trained |ClassificationPartitionedModel| SVM classifier. % The positive class is |'g'|. The |ScoreTransform| property is |none|. %% % Estimate the optimal score function for mapping observation scores to % posterior probabilities of an observation being classified as |'g'|. [ScoreCVSVMModel,ScoreParameters] = fitSVMPosterior(CVSVMModel); ScoreTransform = ScoreCVSVMModel.ScoreTransform ScoreParameters %% % |ScoreTransform| is the optimal score transform function. % |ScoreParameters| contains the score transformation function, slope % estimate, and the intercept estimate. %% % You can estimate test-sample, posterior probabilities by passing % |ScoreCVSVMModel| to |kfoldPredict|.