Second Order Sobol Indices

I am trying to produce second order Sobol indices for feature selection. I am looking for the features that best predict the binary outcome variable. I keep getting an error saying that my iteration reached because the estimated coefficients perfectly separate failures from successes.

Here is my code:

for i=1:1000

Mdl=fitglm([xt(:,i) xt(:,i).^2],yt,'distribution','binomial');

dyhat=predict(Mdl,[xt(:,i) xt(:,i).^2]);

dyhat=dyhat>0.5;

te(:,i)=sum(dyhat(1:n0)>0)+sum(dyhat(101:end)<1);

[~,I]=sort(te);

end

Are there obvious bugs that I am missing? Thank you for any help.