[scikit-learn] Opinion on reference mentioning that RF uses weak learners

[Matthieu Brucher]

Hi,

What are you wondering?
The individual tree is weakened by design (accepts more errors), so
indeed, the individual trees are weak learners and the combination of
them (the forest) becomes the strong learner.
You can have a strong tree as well (deeper, more parameters), but
that's not what is searched in a random forest.

Cheers,

Matthieu

Le dim. 16 août 2020 à 19:06, Fernando Marcos Wittmann
<fernando.wittmann at gmail.com> a écrit :
>
> Hello guys,
>
> The the following reference states that Random Forests uses weak learners:
> - https://blog.citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics#:~:text=The%20random%20forest%20starts%20with,corresponds%20to%20our%20weak%20learner.&text=Thus%2C%20in%20ensemble%20terms%2C%20the,forest%20is%20a%20strong%20learner
>
>> The random forest starts with a standard machine learning technique called a “decision tree” which, in ensemble terms, corresponds to our weak learner.
>>
>> ...
>>
>>  Thus, in ensemble terms, the trees are weak learners and the random forest is a strong learner.
>
>
> I completely disagree with that statement. But I would like the opinion of the community to double check if I am not missing something.
>
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