# In Lopez de Prado's Advances in Financial Machine Learning, what is meant by “unnecessary 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?

• A very warm welcome to Quant.SE. I think that this is misleading at best and wrong at worst. On a simlilar note see: blog.ephorie.de/…. – vonjd Jan 31 at 17:08
• Thank you for the warm welcome. Can you please elaborate - what is misleading/wrong? The quoted text implying multiple labels? The book quoted? My interpretation of it? Other? – Lafayette Jan 31 at 18:08
• Regarding the "ZeroR classifier" - this is indeed an elegant way to represent the importance of class balance in judging accuracy. Thanks for sharing :) – Lafayette Jan 31 at 18:11
• To drop labels! – vonjd Feb 1 at 14:22
• Ah, Okay. Still, the question remains - the text hints at the existence of multiple labels - what are they? Long/Flat/Short and Go/No-Go do not justify the process described. – Lafayette Feb 2 at 17:22