www.gusucode.com > stats 源码程序 matlab案例代码 > stats/ExploreFeatureSelectionNCAClassificationObjectExample.m
%% Explore |FeatureSelectionNCAClassification| Object % Load the sample data. load ionosphere %% % The data set has 34 continuous predictors. The response variable is the % radar returns, labelled as b (bad) or g (good). %% % Fit a neighborhood component analysis (NCA) model for classification to detect % the relevant features. mdl = fscnca(X,Y); %% % The returned NCA model, |mdl|, is a % |FeatureSelectionNCAClassification| object. This object stores % information about the training data, model, and optimization. You can % access the properties of this object using dot notation. For example, you % can access the feature weights stored in a % |FeatureSelectionNCAClassification| object. %% % Plot the feature weights. figure() plot(mdl.FeatureWeights,'ro') xlabel('Feature index') ylabel('Feature weight') grid on %% % The weights of the irrelevant features are zero. The |'Verbose',1| option % in the call to |fscnca| displays the optimization information on the % command line. You can also visualize the optimization process by plotting % the objective function versus the iteration number. figure; plot(mdl.FitInfo.Iteration,mdl.FitInfo.Objective,'ro-'); grid on; xlabel('Iteration Number'); ylabel('Objective'); %% % The |ModelParameters| property is a |struct| that contains more information % about the model. You can access the fields of this property using % dot notation. For example, see if the data was standardized or not. mdl.ModelParameters.Standardize %% % |0| means that the data was not standardized before fitting the NCA model. % You can standardize the predictors when they are on very different % scales using the |'Standardize',1| name-value pair argument in the call % to |fscnca| .