KSVM.m

% Kernelized Support Vector Machine

data_training = readtable("dataset_training.txt");
data_testing = readtable("dataset_testing.txt");

% Extract test labels for F1 Score computation
test_labels = data_testing(:, 13);

% Train the classifier
[classifier, score] = trainClassifier(data_training);

% Run classifier on test data
predictions = classifier.predictFcn(data_testing);

% Generate confusion matrix
confusionchart(table2array(test_labels), predictions);
cm = confusionmat(table2array(test_labels), predictions);

TP = cm(1,1);
FP = cm(2,1);
FN = cm(1,2);

F1_Score = (2*TP) / (2*TP + FP + FN)

function [trainedClassifier, validationAccuracy] = trainClassifier(trainingData)
% [trainedClassifier, validationAccuracy] = trainClassifier(trainingData)
% Returns a trained classifier and its accuracy. This code recreates the
% classification model trained in Classification Learner app. Use the
% generated code to automate training the same model with new data, or to
% learn how to programmatically train models.
%
%  Input:
%      trainingData: A table containing the same predictor and response
%       columns as those imported into the app.
%
%  Output:
%      trainedClassifier: A struct containing the trained classifier. The
%       struct contains various fields with information about the trained
%       classifier.
%
%      trainedClassifier.predictFcn: A function to make predictions on new
%       data.
%
%      validationAccuracy: A double containing the accuracy in percent. In
%       the app, the History list displays this overall accuracy score for
%       each model.
%
% Use the code to train the model with new data. To retrain your
% classifier, call the function from the command line with your original
% data or new data as the input argument trainingData.
%
% For example, to retrain a classifier trained with the original data set
% T, enter:
%   [trainedClassifier, validationAccuracy] = trainClassifier(T)
%
% To make predictions with the returned 'trainedClassifier' on new data T2,
% use
%   yfit = trainedClassifier.predictFcn(T2)
%
% T2 must be a table containing at least the same predictor columns as used
% during training. For details, enter:
%   trainedClassifier.HowToPredict

% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
inputTable = trainingData;
predictorNames = {'Var1', 'Var2', 'Var3', 'Var4', 'Var5', 'Var6', 'Var7', 'Var8', 'Var9', 'Var10', 'Var11', 'Var12'};
predictors = inputTable(:, predictorNames);
response = inputTable.Var13;

% Train a classifier
% This code specifies all the classifier options and trains the classifier.
classificationSVM = fitcsvm(...
    predictors, ...
    response, ...
    'KernelFunction', 'polynomial', ...
    'PolynomialOrder', 3, ...
    'KernelScale', 'auto', ...
    'BoxConstraint', 1, ...
    'Standardize', true, ...
    'ClassNames', [-1; 1]);

% Create the result struct with predict function
predictorExtractionFcn = @(t) t(:, predictorNames);
svmPredictFcn = @(x) predict(classificationSVM, x);
trainedClassifier.predictFcn = @(x) svmPredictFcn(predictorExtractionFcn(x));

% Add additional fields to the result struct
trainedClassifier.ClassificationSVM = classificationSVM;

% Perform cross-validation
folds = 5;
partitionedModel = crossval(trainedClassifier.ClassificationSVM, 'KFold', folds);

% Compute validation predictions
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);

% Compute validation accuracy
validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');

end