I am wondering how random forests are exactly implemented in Weka. This paper is very specific about RFs in Weka, but the description of its learning process in chapter 2 seems strange to me. They say:
- Bootstrap samples $B_i$ for every tree $t_i$
- A random subset of features is selected for each $t_i$
- Information gain is used to grow unpruned trees $t_i$
- Shouldn't step 2 be repeated on all levels of the decision tree? Otherwise each tree will never see some of the features
- Whats the default when setting
numFeatures=0. I think this is the number of features that is available for each split. Is it the square root of the number of all features?
- Is really information gain used for determining the best split attribute?
I am using Weka 3.8.3 - not sure if this matters.
Thanks for all hints :)