> 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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