Goodman's grue-like concerns about projectibility apply here: concepts redefined per loss function lack the entrenchment needed to support inductive inference across modeling contexts.

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Loss function(as used in machine learning): A mathematical tool that measures how wrong a model's predictions are—the bigger the loss, the worse the predictions.
Modeling contexts(Refers to whether concepts can reliably transfer between different prediction tasks): Different situations or frameworks where you're trying to make predictions or understand a pattern—like different machine learning models or different real-world problems.
Nelson Goodman(the philosopher whose theory is being discussed): A 20th-century American philosopher who developed theories about how symbols (like words, pictures, and artworks) work and mean things.
entrenchment(Goodman's account of why pictures are classified as they are): The historical process by which certain predicates (pictorial or verbal) come to be projected and applied rather than other available predicates, explaining how pictorial classifications become established.

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