The answer is simple : you don't need it since there is no local optima to stuck in with different random seeds
because generally in logistic regression problems; there is a global optimum.
I used 3 different algorithms (Linear Regression, Logistics Regression, Decision Tree) to solve the same prediction problem and I have to compare their error measures. The problem at first was that the MAE, MSE, and RMSE values kept changing with each run, it was really problematic for me. The suggested solution was to use random_state.
The "random_state" argument works for Logistic Regression and Decision Tree but Linear Regression doesn't take this argument. In that case, how do I keep the error measure values from changing? Is there any alternative to "random_state" for Linear Regression?
Alternative to "random_state" for Linear Regression?