random forest classifier - impact of small n_estimator and repeated training trying to have a better understanding of random forest algorithm here. With the same training and holdout datasets, I tried two things here:


Set a small n_estimator (10), train on my training dataset and apply to my holdout dataset. If I repeat this several times, the result (e.g. correctly predicted target class) varies somewhat from run to run. My understanding is that since the # of trees is small in my model, there are variations in my model after training thus leading to different results. 
Set a high n_estimator (300) and do the same. Then the results don't vary. My take is that impact of high n_estimator reduces variation in the model and thus i get the same prediction every time. 


So if I run my scenario 1 a bunch of times and consolidate the results (i.e. run 1 predicts A B in class 1, run 2 predicts A C in class 1, run 3 predicts D in class 1), my final results would be A B C D are in class 1. My question is:
1. Is this essentially the same as running it once with a large n_estimator?
2. Is this approach problematic because I am relying more on "guessing" (e.g. small n_estimator leads to larger variation in outcomes)? 

Thanks!
there is a parameter in estimators that is called random_state that fixes a seed for your algorithm run and reruns exactly the same so you can expect the same results at every rerun of the exact same code. It makes your code deterministic. But yes, in random forests in particular, due to their nature of averaging across all trees created when the forest is grown, variance in your result becomes less evident. So your reasoning is correct.

If you want to make your low_estimators RF produce the same results at every run, just add random_state and give it a random number