My understanding is that if we have n_estimator is more than 1 then OOB_score is a average of all the tree .

Or is there any better or correct way to explain this .

# [ML video] How is OOB_score and n_estimator are related

**satish860**(satish) #1

**ecdrid**(Aditya) #2

I will give it a shot,

The oob score error is the average error for each z_i calculated using predictions from the trees that do not contain z_i in their respective bootstrap sample…

Probability is `(2/3)`

It’s straight from the wonderful curated sklearn docs…

**satish860**(satish) #3

Ok to restate what you have written and associate with the Bull dozer problem

When the predication of unit sale value for 10 trees are [9000,8000,…] and so forth and the average of the 8500 and the actual value is 8400 the error will be 100 and OOB score will be 0 % .

Is my understanding correct ?