I'm not sure honestly, but I think you'll find more details in
Schapire's paper (http://rob.schapire.net/papers/strengthofweak.pdf) and
its refs. In particular page 5 (201)
On 8/16/20 8:37 PM, Brown J.B. via scikit-learn wrote:
> As previously mentioned, a "weak learner" is just a learner that
barely performs better than random.
To continue with what the definition of a random learner refers to,
does it mean the following contexts?
(1) Classification: a learner which uniformly samples from one of the
N endpoints in the training data (e.g., the set of unique values in
the response vector "y").
(2) Regression: a learner which uniformly samples from the range of
values in the endpoint/response vector (e.g., uniform sampling from
[min(y), max(y)]).
Should even more context be explicitly declared (e.g., not uniform
sampling but any distribution sampler)?
J.B.
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