Data Science Asked on October 1, 2021
I have all kinds of machine learning terms that co-occur with the word “agnostic”, including model-agnostic learning, model-agnostic metric. From the dictionary, it explains the word “agnostic” in the following way
a person who holds the view that any ultimate reality (such as God) is unknown and probably unknowable.
which does not make those terms more understandable.
In some contexts, I find that “agnostic” refer to “generic” or “free of”. For example, in the paper I am reading now, the authors define a threshold-agnostic metric, where they use score rather than hard 0/1 assignment for the task.
However, I am wondering if there is formal definition for the word “agnostic” in the machine learning community.
I know of the model-agnostic term, and a close meaning would be model-independent. Basically, when you are studying a machine learning problem, the underlying structure in the data may or may not be described by one type of model or the other. The model-agnostic approach consists in using machine learning models to study the underlying structure without assuming that it can be accurately described by the model because of its nature. This avoids introducing a potential bias in the interpretation.
A model-agnostic approach pretty much requires that several different techniques are used for the same task.
Answered by Romain Reboulleau on October 1, 2021
"Agnostic" means that nothing is known or can be known of the existence.
In data science, the term "Agnostic" is used when there's self-modelling/self-learning technique is involved.
For e.g.
A robot modeled itself without prior knowledge of physics or its shape and used the self-model to perform tasks and detect self-damage [1]. It is task agnostic self modeling.
It is reminiscent of meta learning.
Answered by Nischay Namdev on October 1, 2021
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