In Lopez de Prado's Advances in Financial Machine Learning, Chapter 3, Prof. Lopez de Padro talks about dropping rare labels:
Some ML classifiers do not perform well when classes are too imbalanced. In those circumstances, it is preferable to drop extremely rare labels and focus on the more common outcomes. Snippet 3.8 presents a procedure that recursively drops observations associated with extremely rare labels. Function dropLabels recursively eliminates those observations associated with classes that appear less than a fraction minPct of cases, unless there are only two classes left.
I fail to see what could that mean, seeing as (if I understood correctly), the labels are either Go/No Go (for metalabeling) or Short/Flat/Long for "prime" labeling.
Dropping rare labels until only two are left is meaningless for Go/No Go, and as for Short/Flat/Long labeling - while it is possible to apply the method and drop one of the classes if too rare, it seems to me the the implication of the language of the quote suggests that the author expects more then these three labels (otherwise the language of the quote would probably refer to it) - to speak nothing of the fact that an expectation of three labels, only one of which could be dropped would render the use of a recursive approach irrelevant).
So I can summarize that the author expects different types of labels, of which more then 3 are expected. What are those labels and how are they generated?