Statsmodel logit with sample weights Using sklearn I can consider sample weights in my model, like this:
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression(solver='liblinear')
logreg.fit(X_train, y_train, sample_weight=w_train)

Is there some clever way to consider sample weights also in the Logit method of statsmodel.api?
import statsmodels.api as sm
logit = sm.Logit(y, X)
It seems that there is a way of using sample weights which requires a little more work than just using a single argument, see this stackoverflow answer.