Change the axis limits of a SVM plot
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NC
le 27 Juin 2018
Modifié(e) : Jonathon Gibson
le 28 Juin 2018
I trained a SVM classifier using "fitcsvm" and I got the graph shown below when the data was plotted. I want to make it more readable by reducing the range of axis. How to do it? The code I used is given below and the used datasets are attached.
close all; clear all;
load ImageDataSet.csv load ImageDataSetLabels.csv load PhotoshopPredict.csv
%grp_idx = grp2idx(FeatureLabels);
X = ImageDataSet(1:1763,:); y = ImageDataSetLabels(1:1763,:); X_new_data = PhotoshopPredict(1:end,:);
%dividing the dataset into training and testing rand_num = randperm(1763);
%training Set X_train = X(rand_num(1:1410),:); y_train = y(rand_num(1:1410),:);
%testing Set X_test = X(rand_num(1411:end),:); y_test = y(rand_num(1411:end),:);
%preparing validation set out of training set
c = cvpartition(y_train,'k',5);
SVMModel = fitcsvm(X_train,y_train,'Standardize',true,'KernelFunction','RBF',... 'KernelScale','auto','OutlierFraction',0.05);
CVSVMModel = crossval(SVMModel); classLoss = kfoldLoss(CVSVMModel) classOrder = SVMModel.ClassNames
sv = SVMModel.SupportVectors;
figure gscatter(X_train(:,1),X_train(:,2),y_train) hold on plot(sv(:,1),sv(:,2),'ko','MarkerSize',10) legend('Resampled','Non','Support Vector') hold off
X_test_w_best_feature =X_test(:,:); [c,score] = predict(SVMModel,X_new_data);
saveCompactModel(SVMModel,'SVM1000Images');
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Jonathon Gibson
le 27 Juin 2018
Modifié(e) : Jonathon Gibson
le 27 Juin 2018
To change the axis limits, you can add axis([xmin xmax ymin ymax]) to the end of your script. This will make some of the higher points not visible, but gives you a better sense of the data:
axis([0 200 0 5000]);
Because the data is so spread out, it might help to instead use a logarithmic scale on your axes:
axis tight;
set(gca,'yscale','log');
set(gca,'xscale','log');
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Jonathon Gibson
le 28 Juin 2018
Modifié(e) : Jonathon Gibson
le 28 Juin 2018
I believe that your sv was smaller than expected and you are getting the wrong predictions because you train on standardized data, but then try to test and plot the raw unstandardized data. Change to this to see the difference:
SVMModel = fitcsvm(X_train,y_train,'Standardize',false,'KernelFunction','RBF',...
'KernelScale','auto','OutlierFraction',0.05);
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