Comments (6)
I confirm that it is an inconsistency between those two. However, it has no impact in the prediction value.
To sum it up:
- query.py: result[0][1] = probability of the 1 class to be true (radiant_win) - it's correct
- augment_one_hot.py: strictly speaking, as I do not make queries, but only check the accuracy, how I encode the states is irrelevant
However, I agree that symmetry should be achieved, so I will update the .ipynb by fixing this issue and introducing comments. Thanks a lot for observing the issue!
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I'm not entirely convinced yet. If your y_train encodes that y_train[:,0] = chance of radiant_win and y_train[:,1] = chance of dire_win (second column), how does result[0][1] (second column) encode the chance of radiant_win? Sorry if this is obvious, I just don't see it.
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I realize that I failed to explain properly. What I meant was that the notebook and the rest of the project have nothing to do with each other.
You can consider the query.py encoding the right version with result[0][1] meaning the chance of radiant_win, and the one from the notebook the wrong version. However, since in the notebook we don't make queries regarding radiant/dire, it has no impact on the accuracy.
I will fix the notebook anyway so further confusion is avoided. I hope I was clear enough this time, but if I was not, feel free to ask.
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Basically, at the moment, logistic regression (query.py) predicts [dire_chance, radiant_chance] and the notebook predicts [radiant_chance, dire_chance].
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Oh alright now I understand. I used the notebook code to create x_train and y_train for my model, but you created it differently for your model, I assume, so for you it is consistent. Thanks for clearing it up.
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I added two better explained IPython notebooks in the "experiments" folder. The one hot encoding was removed such that there is no more confusion.
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