In these notes, we have the following statement
The depth of a learned decision tree can be larger than the number of training examples used to create the tree
This statement is false, according to the same notes, where it is written
False: Each split of the tree must correspond to at least one training example, therefore, if there are $n$ training examples, a path in the tree can have length at most $n$
Note: There is a pathological situation in which the depth of a learned decision tree can be larger than number of training examples $n$ - if the number of features is larger than $n$ and there exist training examples which have same feature values but different labels.
I had written on my notes that the depth of a decision tree only depends on the number of features of the training set and not on the number of training samples. So, what does the depth of the decision tree depend on?