You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
We do a full-on multi-dimensional optimization to get logistic regression parameters via likelihood maximization. I don't see any alternatives in the literature, but we should at least be able to make a better initial guess to feed into that algorithm than "all zeros", which is what we currently do.
The text was updated successfully, but these errors were encountered:
For the bivariate case, here is a simple idea I found online. Call x_T and x_F the mean values of x for the true and false cases. Assume
\sigma(a + b x_T) = 3/4
\sigma(a + b x_F) = 1/4
Then b = \frac{\sigma^{-1}(3/4) - \sigma^{-1}(1/4)}{x_T - x_F}. This should get the sign right, and the order of magnitude if we are lucky.
We do a full-on multi-dimensional optimization to get logistic regression parameters via likelihood maximization. I don't see any alternatives in the literature, but we should at least be able to make a better initial guess to feed into that algorithm than "all zeros", which is what we currently do.
The text was updated successfully, but these errors were encountered: