Logistic regression using SciPy

Question

I am trying to code up logistic regression in Python using the SciPy fmin_bfgs function, but am running into some issues. I wrote functions for the logistic (si

Answer 1

I was facing the same issues. When I experimented with different algorithms implementation in scipy.optimize.minimize , I found that for finding optimal logistic regression parameters for my data set , Newton Conjugate Gradient proved helpful. Call can be made to it like:

Result = scipy.optimize.minimize(fun = logLikelihoodLogit, 
                                 x0 = np.array([-.1, -.03, -.01, .44, .92, .53,1.8, .71]), 
                                 args = (mX, vY),
                                 method = 'TNC',
                                 jac = likelihoodScore);
optimLogit = Result.x;