Can weak-learner be defined in the case of regression problem? weak-learner is often defined relating to PAC learning.
However, to the extent I know, I have never seen the definition of weak-learner when regression.
That is, the definition of weak-learner on wikipedia is as following.
"A weak learner is defined to be a classifier that is only slightly correlated with the true classification", which is defined only when $f$ is classifier.
Is this definition extended to regression case?


weak-learner is often defined relating to PAC learning.

PAC learning only consider binary classification to build a learning theory. But it can be applied to any supervised learning setting.

Is this definition extended to regression case?

Naively, we can extent this definition to regression models. Since, by weak-learning we imply poorly performing model but slightly better than random guess. A regression model can be considered a weak-learner, with the following conditions.

*

*Consider percentage-error type measure of performance.

*50% recent-error could be considered as a random-guess learner.

*Weak-learner would give slightly higher percentage error than a random-guess.

