svm fo 2 pairs class in matlab

To train a Support Vector Machine (SVM) for a binary classification problem in MATLAB, you can use the built-in fitcsvm function. Here's an example of training an SVM for a two-class problem:

main.m
% Define your input features (X) and corresponding labels (Y)
X = [your_feature_values];
Y = [your_labels];

% Split the data into training and testing sets
cv = cvpartition(size(X,1),'HoldOut',0.2); % 80-20 split
idx = cv.test;
Xtrain = X(~idx,:);
Ytrain = Y(~idx,:);
Xtest  = X(idx,:);
Ytest  = Y(idx,:);

% Train the SVM model
SVMModel = fitcsvm(Xtrain, Ytrain);

% Predict the labels for the test set
Ypred = predict(SVMModel, Xtest);

% Evaluate the model performance
accuracy = sum(Ypred == Ytest) / numel(Ytest);
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Make sure to replace [your_feature_values] and [your_labels] with your actual data. Also, note that this example uses a hold-out validation strategy with an 80-20 split.

You can further customize the SVM training by specifying options and tuning hyperparameters using the fitcsvm function arguments. Refer to the MATLAB documentation for more details.

Please note that the example provided assumes that you have the Statistics and Machine Learning Toolbox installed in your MATLAB environment.