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It's because the test dataset involves randomly choosing samples spread throughout the time series. Imagine dropping 10% of the pixels randomly spaced out in a
by bcheung 9y ago
It's because the test dataset involves randomly choosing samples spread throughout the time series.
Imagine dropping 10% of the pixels randomly spaced out in a historical graph and then try to fill them in. It's trivial because you can just average the previous and next sample and have an extremely high accuracy.
- claytonjy 9y agoI don't think that's true, because of the `shuffle = False` argument to `train_test_split`; it's just chopping the tail off for the test set.