Random state in machine learning models
If i understand correctly you want to make sure your results will remain constant and won't change?
Or in other words, are you trying to make sure your results are reproducible?  

If so, this FAQ in the scikit-learn site has exactly what you are looking for.

I'll also repeat what it says here:
An algorithm might have multiple points that introduce randomness to the process and thus introduce randomness to the result.  

One method to make sure your result are constant is to set every possible random_state available in the functions that you use.
The risk in this method is that you might miss some of the places that uses a seed.

The second method, which they also describe in the FAQ, is to set a global seed once.
scikit-learn will use it for all it's random processes.
So setting a global seed like this

import numpy as np
np.random.seed(42)


will make your results constant and reproducible.