www.gusucode.com > stats 源码程序 matlab案例代码 > stats/ExploreFeatureSelectionNCARegressionObjectExample.m
%% Explore |FeatureSelectionNCARegression| Object % Load the sample data. load imports-85 %% % The first 15 columns contain the continuous predictor variables, whereas % the 16th column contains the response variable, which is the price of a car. Define % the variables for the neighborhood component analysis model. Predictors = X(:,1:15); Y = X(:,16); %% % Fit a neighborhood component analysis (NCA) model for regression to detect % the relevant features. mdl = fsrnca(Predictors,Y); %% % The returned NCA model, |mdl|, is a % |FeatureSelectionNCARegression| 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 % |FeatureSelectionNCARegression| 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 |fsrnca| 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 also 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 |fsrnca| .