Early Stopping Strategy

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Early stopping monitors validation loss and halts training when it stops improving. What key risk does this technique address, and what is the main hyperparameter involved?

Multiple Choice

It prevents overfitting; the patience parameter controls how many epochs without improvement to wait before stopping It prevents underfitting; the threshold parameter defines the minimum acceptable training loss It prevents gradient explosion; the tolerance parameter sets the maximum allowed gradient norm It prevents catastrophic forgetting; the checkpoint interval defines how often to save the model

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Early Stopping Strategy

Multiple Choice

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