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Currently early stop has two strategies that only trigger if a validation metric decreases with two criteria:
There are two strategies: 1) consecutive_increase, early stop is triggered if the current validation score is lower than the average of the last early_stop_rounds validation scores and 2) average_increase, early stop is triggered if for the last early_stop_rounds consecutive steps, the validation scores are decreasing.
However there is the possibility of a metric becoming "stuck" with very minor increases to its value e.g.
To address this case we should add relative increase parameter that will trigger early stop also in the cases where the metric has not improved for more than e.g. 1%. That way we can stop training when a metric plateaus.
The text was updated successfully, but these errors were encountered:
Currently early stop has two strategies that only trigger if a validation metric decreases with two criteria:
However there is the possibility of a metric becoming "stuck" with very minor increases to its value e.g.
To address this case we should add relative increase parameter that will trigger early stop also in the cases where the metric has not improved for more than e.g. 1%. That way we can stop training when a metric plateaus.
The text was updated successfully, but these errors were encountered: