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Moved from the previous repository (author @wouterjdb ):
It's hard to guess from the start how many iterations you need. After we a metric describing the training and validation phases, we should be able to create an automatic stopping algorithm:
Continue the history matching iterations as long as both the training and validation metrics show improvements. The moment that the validation metric performs worse than in the previous iterations (over fitting) one should stop.
The text was updated successfully, but these errors were encountered:
Moved from the previous repository (author @wouterjdb ):
It's hard to guess from the start how many iterations you need. After we a metric describing the training and validation phases, we should be able to create an automatic stopping algorithm:
Continue the history matching iterations as long as both the training and validation metrics show improvements. The moment that the validation metric performs worse than in the previous iterations (over fitting) one should stop.
The text was updated successfully, but these errors were encountered: