function [rusTree, istrain, istest] = runSRB(X,Y)

%RUNSRB Summary of this function goes here

%   Detailed explanation goes here

%% Transform data into a usable form

    %Semantic labels

    label = 'Sphericity';

    %Show distribution for semantic label

    fprintf('%s%s\n','Tabulation for: ', label);

    tabulate(Y);

    %create weak learner template for RUSBoosting to build an ensemble on

    % TODO: CHANGE THESE PARAMS TO SOMETHING ELSE

    t = ClassificationTree.template();

%% Build testing and training sets

    %build training and testing sets

    part = cvpartition(Y,'holdout',0.25);

    istrain = training(part);

    istest = test(part);

%% Run RUSBoosting

    %build training sets

    trainingX = X(istrain,:);

    trainingY = Y(istrain);

    %Run a decision tree

    %tree = ClassificationTree.fit(trainingX,trainingY);

    %run the RUSboost on a semantic label

    rusTree = fitensemble(trainingX,trainingY,'RUSBoost',1000,'Tree',...

        'LearnRate',1,'nprint',100);

%% Plot error rate

    %build testing sets

    testingX = X(istest,:);

    testingY = Y(istest);

    figure;

    plot(loss(rusTree,testingX,testingY,'mode','cumulative'));

    grid on;

    xlabel('Number of trees');

    ylabel('Test classification error');

    disp('end');

end