Second Order Sobol Indices
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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.
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