--- a
+++ b/classification/RUSboost/runSRB.m
@@ -0,0 +1,55 @@
+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
+