Feeding a seed value to solver in Python Logistic Regression
You can use the warm_start option (with solver not liblinear), and manually set coef_ and intercept_ prior to fitting.

warm_start : bool, default=False
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. Useless for liblinear solver.
I am using scikit-learn's linear_model.LogisticRegression to perform multinomial logistic regress. I would like to initialize the solver's seed value, i.e. I want to give the solver its initial guess as the coefficients' values.
Does anyone know how to do that?  I have looked online and sifted through the code too, but haven't found an answer.
Thanks!