GenerateModel_RandomForest.m

function [trainedClassifier, validationAccuracy] = GenerateModel_RandomForest(Learners_values, Labels, Model_parameters, partition_for_CV, Class_list, Learners_names)
% [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 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

% Auto-generated by MATLAB on 25-Aug-2017 12:32:19

MaxNumSplits = Model_parameters{1};
NumLearningCycles = Model_parameters{2};

template = templateTree('MaxNumSplits', MaxNumSplits);

classificationEnsemble = fitcensemble(...
    Learners_values, ...
    Labels, ...
    'Method', 'Bag', ...
    'NumLearningCycles', NumLearningCycles, ...
    'Learners', template, ...
    'ClassNames', categorical(Class_list),...
    'PredictorNames', Learners_names);

trainedClassifier.ClassificationEnsemble = classificationEnsemble;

if ~isempty(partition_for_CV)
    % Perform cross-validation
    partitionedModel = crossval(trainedClassifier.ClassificationEnsemble, 'CVpartition', partition_for_CV);
    
    % Compute validation accuracy
    validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');
    disp(['Accuracy: ',num2str(100*validationAccuracy,'%.1f'),' %']);
else
    validationAccuracy = [];
end

end