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Sep 15, 2020 at 8:57 comment added John Hi, no I mean "learning curve" it terms of the model’s performance on the training set and the validation set as a function of the training set size not with each step. But to be more clear I am confused about the workflow of a ML binary classifcation problem. Do I evaluate the learning curve before, after or before and after I fine-tune a specific model? And when comparing different classification models should I fine-tune all of them before comparing them to eachother?
Sep 15, 2020 at 6:55 history edited noe CC BY-SA 4.0
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Sep 14, 2020 at 22:26 history answered noe CC BY-SA 4.0