@CamDavidsonPilon Let me know if you have any suggestions for question below:
Hi, I am using CoxPHFitter with IPS weights and
robust=Trueflag. However, the fit is taking really long time to finish. I have about million instances and 6 features in my dataset. Let me know if slower runtime is expected in weighted version and what can be done to speed it up.
Hi all. I've somewhat new to using lifelines, and in using the CoxPHFitter, when I run
check_assumptions, I end up with an error that reads as follows:
/RuntimeWarning: overflow encountered in exp scores = weights * np.exp(np.dot(X, self.params_))
Any suggestions on dealing with this issue? I'm starting down the road of normalization, but I'm not sure if that's 100% correct.
i just raised your
conditional_after for CoxPH. it felt like running into a wall after reading about the new argument in the docs. i even bit the bullet and switched from the conda to the pip package ;)
your commit message does not sound to hopeful for that one, are you still working on it?
ps: still an awesome library