The original meaning of “Ablation” is the surgical removal of body tissue. The term “Ablation study” has its roots in the field of experimental neuropsychology of the 1960s and 1970s, where parts of animals’ brains were removed to study the effect that this had on their behaviour.
    In the context of machine learning, and especially complex deep neural networks, “ablation study” has been adopted to describe a procedure where certain parts of the network are removed, in order to gain a better understanding of the network’s behaviour.

    Now, the term “ablation study” is often used in the context of neural networks, especially relatiavely complex ones such as R-CNNs. The idea is to learn about the network by removing parts of it and studying it’s performance. In the context of linear regression that you propose, ablation doesn’t really make sense - it would turn into a kind of backwards stepwise variable selection procedure.

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What is an ablation study? And is there a systematic way to perform it?