function tree = DecisionTreeLearning (features , labels)
    x = 1;
    line = 1;
    if(size(labels,1) == 0)
        tree.op = [];
        tree.kids = [];
        tree.class = [];
        tree.attribute = [];
        tree.threshold = [];
        return
    end
    for line = 2 : size(labels,1) %Check to see if all of the labels are the same
        %labels(line,:)
        %labels(x,:)
        %labels(line,:)~=labels(x,:)
        if (ismember(1,labels(line,1)~=labels(x,1))) %Checks by seeing if any are different
            break
        end
        x = x + 1 ;
    end
    y = size(labels,1)
    if(x == line)%If all labels are the same, then create a leaf node
        tree.op = [];
        tree.kids = [];
        tree.class = labels(x-1,:);
        tree.attribute = [];
        tree.threshold = [];
        return
    end
    if(size(labels,1) == 1)%If all labels are the same, then create a leaf node
        tree.op = [];
        tree.kids = [];
        tree.class = labels(x,:);
        tree.attribute = [];
        tree.threshold = [];
        return
    end


    [attribute, threshold] = ChooseAttribute(features , labels); %Choose the attribute and threshold to test

    [lessThanFeatures,lessThanLabels,greaterThanFeatures,greaterThanLabels] = Split(features, labels , attribute , threshold); %Split the data set according to attribute and threshold


    tree.op = num2str(attribute);
    tree.attribute = attribute;
    tree.threshold = threshold;
    tree.class = [];
    tree.kids = {DecisionTreeLearning(lessThanFeatures,lessThanLabels),DecisionTreeLearning(greaterThanFeatures,greaterThanLabels)}; %Create the branches with new trees with the split data