在 matlab 中使用 SVM 对新数据进行预测

Question

我使用“fitcsvm”函数训练了一个 SVM 分类模型，并使用测试数据集进行了测试。现在我想用这个模型来预测新的（以前看不见的）数据的类别。应该怎么做？

以下是我使用的代码。

load FeatureLabelsNum.csv
load FeatureOne.csv

X = FeatureOne(1:42,:);
y = FeatureLabelsNum(1:42,:);

%dividing the dataset into training and testing 
rand_num = randperm(42);

%training Set
X_train = X(rand_num(1:34),:);
y_train = y(rand_num(1:34),:);

%testing Set
X_test = X(rand_num(34:end),:);
y_test = y(rand_num(34: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(:,:);
bp = (predict(SVMModel,X_test)== y_test);

Answer 1

您已经在脚本中使用了 predict 函数，但是，只需传入新数据， score 就会包含您的预测标签。

[~,score] = predict(SVMModel,X_new_data);