Huy, here is a complete demo along with instructions in the comments
%==============================================================================
% TRAINING
trainingData=readtable("ClassificationData.xlsx")
% The first 4 columns are the inputs.
tPredictors = trainingData(:, 1:4);
% The last column is the "answer/ground truth".
tResponse = trainingData{:, end};
% Now run Classification Learner app. Start a new session from workspace, and
% Select tPredictors as the "Data set variable" and
% select tResponse as the response.
% I did "All quick to train" and found the Fine Tree model was the best.
% I highlighted it and clicked the Export button to export trainingModel to the workspace.
% Then save it to a .mat file from the command line like this:
% save('trainedModel.mat', 'trainedModel')
%==============================================================================
% TESTING
% Now run it on some test data once you have the trainedModel.mat file created.
% First read in the model from the .mat file
fileName = fullfile(pwd, 'trainedModel.mat');
if ~isfile(fileName)
message = sprintf('Model file not found:\n%s\n\nAre you sure you exported it from Classification Learner', fileName);
uiwait(errordlg(message));
return;
end
s = load(fileName)
trainedModel = s.trainedModel;
% Read in test data
testData=readtable("ClassificationTestData.xlsx")
testPredictors = testData(:, 1:4); % First 4 columns are the model inputs.
testResponse = testData{:, end}; % Last column is the ground truth (correct values).
% Predict the values.
numTestValues = size(predictions, 1)
% Compare predictions to ground truth.
itsCorrect = zeros(numTestValues, 1);
for k = 1 : numTestValues
if isequal(predictions{k}, testResponse{k})
itsCorrect(k) = 1;
end
end
% accuracy
accuracy = sum(itsCorrect)*100/numTestValues
