Bootstrapping vs. K-Fold: Is every data point in atleast one of the test set/out of bag - atleast once?

It's easy to see that in K-Fold cross-validation, that split training examples into K parts, in such a way that 1 of the K parts is considered to be the test set, and eventually as you shift which parts are in the training and test, you can always guarantee that every single training example is in exactly one of the K different test sets.

I don't know how to make an argument for Bootstrapping! I see that on average each Bootstrap would contain $$\frac{2}{3}$$ of the observations.. so what can I say about how data points may land out of the bag?

• 1 - (1- 1/np.exp(1))**B – usεr11852 Apr 6 at 2:39