On the usage of many train/test split ratios Is there any benefit in evaluating model performance under different train/test ratios?
To me, this sounds kind of nonsense because of these reasons:

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*I cannot access such a parameter when the model is in production, making it pointless to have it in a model selection process;

*How should I interpret a change from one ratio to another? I cannot say "...because the performances are stable across different ratios therefore, my pipeline is more robust.". The test sample will be of different sizes preventing any meaningful testing, especially in classification.

Of course, It may be a confirmation bias of mine. If someone has good reasons, plus some article pointing to that, I will be more than happy to read those.

Based on your description, my opinions are:

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*Try different train/test radio is meaningful. Because, if you can find the lower bound or your ratio, this can show how sufficient your model is to some extent.


*Finding the best radio for your model, can also lead the users to split their datasets.
However, I also think this radio does not make as much sense as one thought it would. Because radio is not universal. For example, suppose we have the best-performing radio is 7:3, but that doesn't mean that you can use this ratio on a very small data set and get the model to perform as expected, so I think it makes more sense to experiment and try to find the minimum amount of data your model needs.
