fitensemble cross-validation error

I want to to find out optimal number of splits and learning rate for LSBoost. I followed the procedure mentioned in fitensemble documentation. However, I am getting an error at the end of the code. Code is as follows

Ntrees=250;
n=size(X,1);
m = floor(log2(n - 1));
lr = [0.01 0.05 0.1 0.25 0.5 1];
maxNumSplits = 2.^(0:m);
Mdl = cell(numel(maxNumSplits),numel(lr));
for k = 1:numel(lr);
  for j = 1:numel(maxNumSplits);
      templ = templateTree('MaxNumSplits',maxNumSplits(j));
      Mdl{j,k} = fitensemble(xc,yc,'LSBoost',Ntrees,templ,...
          'Type','regression','KFold',5,'LearnRate',lr(k));
  end;
end;

 kflAll = @(xc)kfoldLoss(xc,'Mode','cumulative');
 errorCell = cellfun(kflAll,Mdl,'Uniform',false);

I got an error inh last line of code, which is as follows

 Operands to the || and && operators must be convertible to logical scalar values.

 Error in classreg.learning.partition.PartitionedEnsemble/checkEnsembleFoldArgs (line 112)
            if ~isnumeric(folds) || ~isvector(folds) || min(folds)<=0 || max(folds)>kfold

 Error in classreg.learning.partition.RegressionPartitionedEnsemble/kfoldLoss (line 133)
            [mode,folds,partArgs] = checkEnsembleFoldArgs(this,extraArgs{:});

 Error in @(xc)kfoldLoss(xc,'Mode','cumulative')

Can anyone please help me figure out the problem and how to correct it?

Also, I don't really understand last two lines of the code, it would be great help if someone can help me to figure that out.