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@jacobrgardner @gpleiss @Balandat Sorry for the tag, but I have ran out of ideas on this! Has anyone come across fitting problems when dealing with function + derivative observations? Since the first post, I have checked the input/output data again and everything seems to be correct. I have also tried using |
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Hello,
I am fitting two different functions (one with 9 dimensional input and one with 12 dimensional input) for which I have function and derivative observations. The 9D case predicts well, however the 12D case not so much. I have tried several things so far to get the 12D case working better:
Any suggestions on what else I can try to improve the 12D results? I am curious how to scale data when dealing with both function observations and derivatives. In the example below, I have min-max normalized the inputs, standardized the outputs, and then scaled the derivatives accordingly. I have tried adding more data in the 12D case, but that does not improve results much.
Below is the example 9D/12D train and test data and example code that I have used for the datasets and prediction results:
csv_files.zip
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