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iFe1er avatar iFe1er commented on May 24, 2024

@shenweichen

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shenweichen avatar shenweichen commented on May 24, 2024

@iFe1er That's a good idea,but the hyperparameters of each model are not easy to determine because it is difficult to determine a consistent setting. And it is too cumbersome to find the optimal parameters by parameter tuning.
Do you have any good solution?

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iFe1er avatar iFe1er commented on May 24, 2024

@shenweichen I would recommend simply adding testing them with default hyper-parameter. Control the variable such as learning rate and batch size, then compare those algorithm on the same test set. That should be helpful enough and an easy way to do.

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