random_state in random forest
Random forests introduce stochasticity by randomly sampling data and features. Running RF on the exact same data may produce different outcomes for each run due to these random samplings. Fixing the seed to a constant i.e. 1 will eliminate that stochasticity and will produce the same results for each run.
I just learned how to use the machine learning model Random Forest; however, although I read about the random_state parameter, I couldn't understand what it does. For example, what is the difference between random_state = 0   and   random_state = 300
Can someone please explain?