I am using the package scikit-learn to compute a logistic regression on a moderately large data set (300k rows, 2k cols. That's pretty large to me!).
Now, since scikit-learn does not produce confidence intervals, I am calculating them myself. To do so, I need to compute and invert the Hessian matrix of the logistic function evaluated at the minimum. Since scikit-learn already computes the Hessian while optimizing, it'd be efficient if I could retrieve it.
In sklearn.classification.LogisticRegression, Is there any way to retrieve the Hessian evaluated at the minimum value?
Note: This is an intermediate step, and I actually only need the diagonal entries of the inverse of the Hessian. If anyone has a more straightforward way to get there, I'd love to learn it.
