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Leaking future data in would be using t+1 for t, e.g. something like a bi-directional LSTM. I assume he means the actual training dataset had some kind of signa
by laichzeit0 7y ago
Leaking future data in would be using t+1 for t, e.g. something like a bi-directional LSTM. I assume he means the actual training dataset had some kind of signal in the data that was also in the test data.
- sgt101 7y agoPeople do this by doing things like testing that their features contain information in both the training and test set. Because they are not exposing the data directly to the classifier they think that they haven't compromised the test set - but what they have done is increased the chances of a chance correlation.