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    I agree with @javadba and would like to add: Another reason could be data contamination where records from the train set also exist in the test set. Commented Apr 27, 2020 at 13:26
  • I disagree with this; model metrics would be more representative of real-world performance if test data is more accurate than train. The downside is that the model performance would be better if the train data was more accurate. Commented Jul 14, 2022 at 19:52
  • I disagree with the statement that it should not be higher than train. In OP's case, the gap is large enough that it should not be. However, in some cases (such as mine), an adversarially trained model can be expected to have a lower training accuracy than a benign test set. Commented Jul 16, 2022 at 2:53
  • @a6623 Thanks for that clarification ie my statement does not hold in all cases. In particular adversarial models are a different animal Commented Jul 16, 2022 at 15:03
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    @Ethereal I have now run across a scneario that matches your description. The training data contains more difficult to model scenarios than some of the prediction/test only datasets. So some of the latter actually have higher statistical performances in prediction. Commented Nov 1, 2024 at 4:29