In their paper they state:
At each tree node, this is combined with a random choice of a certain number of attributes among which the best one is determined. In the extreme case, the method randomly picks a single attribute and cut-point at each node, and hence builds totally randomized trees whose structures are independent of the target variable values of the learning sample.
If I run
splitrule = "extratrees"but without specifying
mtryI see in the resulting object that
mtry = 8. Doesn’t the algorithm also imply a random choice of
mtryas stated in the article?
On Wikipedia there is a small section about extremely randomized trees. In contrast to the paper, it does not mention that the number of features chosen is random but can be specified, which would be in line with what I see in ranger. Then I wonder: Could I then still call this extremely randomized trees as implemented in ranger according to Geurts et al when writing a paper or is this a "mix" of the default and extremely randomized trees?
Wikipedia furthermore says:
first, each tree is trained using the whole learning sample (rather than a bootstrap sample)
Nevertheless, I get the out of bag error in the fit object of ranger, which in my understanding is impossible if the whole training data is used. Only a cross-validation error would be possible unless in fact bagging is still performed.
So, basically my question is: What exactly is implemented in ranger under the option
splitrule = "extratrees" and why did they deviate from the original paper?