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1

It would be useful to provide the loss over time for both training and testing data set. From your description, it seems like you can minimize the training loss, but the testing performance is not going well. If that is the case, try to regularize the model more. One useful approach is doing data argumentation more. From the comments, it seems we have ...


5

I agree with Franks answer that you have effectively overfit in your approach. An important point: You are only using training and testing. By searching for the approach that performs best on testing, you are fitting to the test data. Any time you use a chunk of data to search through a possible space (whether that’s find best coefficients for a regression ...


4

You have fallen into the common trap of tweaking an analysis until you get a satisfactory result. This process is guaranteed to overstate the value of the predictive instrument, or to make your predictions apply only conditionally. For example, removal of outliers results in a model that is conditional on outliers never appearing in future data to which ...


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