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This repository has been archived by the owner on May 3, 2022. It is now read-only.
Right now, when a model is built, several variations of the model can be built in order to select the hyperparameters that result in the best model according to some evaluation metric. However it might also be useful to further require that the metric meet some threshold before it's published. That way, if the 'best' model were quite bad, it would be rejected.
The text was updated successfully, but these errors were encountered:
so this is another parameter that can be set from oryx.conf or is there more to it? Like the model coming up with a reasonable threshold that evolves with more training data.
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Right now, when a model is built, several variations of the model can be built in order to select the hyperparameters that result in the best model according to some evaluation metric. However it might also be useful to further require that the metric meet some threshold before it's published. That way, if the 'best' model were quite bad, it would be rejected.
The text was updated successfully, but these errors were encountered: