Early Stopping
Early Stopping is a regularization technique that halts a model's training process when its performance on a separate validation dataset stops improving, even if the training loss continues to decrease.
Frequently Asked Questions
How does early stopping work?▼
It monitors validation loss epoch-by-epoch. If validation loss plateaus or starts rising for a set number of epochs (patience), training is aborted.
What is the risk of stopping training too early?▼
Underfitting, where the model has not had enough iterations to capture the core patterns in the training set.
Quick Facts
- CategoryModel Training
- Key ApplicationTraining optimization, overfitting prevention, and resource conservation
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