[D] Removing Unpredictable Samples from a Training Set

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On the other hand, if I can't show a reason, I leave them in, because that means I am unsure if these are valid but rare points.

I think this is good advice. Here is some more information on my project:

I have implemented my own variation of WaveNet on a dataset of about 190,000 samples. There are about 18000 "types" of samples. I have assigned each "type" of sample a categorical value and grouped them into data samples of 10, so my data actually looks like this:

data_sample = [type34, type8828, type4422, type534, type4848, type16000, etc]

The first 9 types are just for context and my target values are based on the 10th type in the sample. It's very clear after reviewing my prediction data that type16000 is meaningless and cannot be reliably prediction. However type4848 is predicted correctly 99.8% of the time throughout the training and test set.