How deeply does Machine Learning Algorithms in Depth cover early stopping?

Defines the stopping signal and illustrates a several-epoch criterion, but does not derive a stopping rule mathematically.

Early stopping

As explained in Machine Learning Algorithms in Depth

Early stopping is a training procedure that terminates optimization when performance on a validation criterion stops improving.

Early stopping is a regularization procedure that ends training when validation loss stops improving. If training loss continues to fall while validation loss rises, further optimization may worsen generalization. Stopping at that point can also save computation. section 10.1 section 10.6

Use validation loss to decide when to stop:

After each epoch, compare validation loss with its best value so far. An epoch is one pass through the training dataset. section 10.1
If validation loss fails to improve for several epochs, stop training; save a model checkpoint rather than relying only on the final epoch. section 10.1 section 10.2.1

MNIST training

In a Keras/TensorFlow multilayer perceptron or LeNet-style CNN, record training and validation loss across epochs and use an EarlyStopping callback. The CNN example stops when validation loss has not improved over several epochs. Its loss and accuracy plots make the training–validation difference visible. section 10.1 section 10.2.1

How Machine Learning Algorithms in Depth covers it

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Also explained in

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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIEarly stopping is an iterative-training termination criterion that halts optimization when validation performance fails to improve for a specified number of iterations.
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Ensemble Methods for Machine LearningEarly stopping is a boosting technique that terminates training after enough base estimators have been added to achieve acceptable validation performance.
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Natural Language Processing in Action, Second EditionEarly stopping is a training regularization technique that terminates optimization when validation performance stops improving.
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Deep Learning with Python, Third EditionA training strategy that stops optimization when validation performance stops improving.
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Evolutionary Deep Learning: Genetic algorithms and neural networksEarly stopping is the termination of an iterative optimization or training process before its configured iteration limit when a stopping condition is met.
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Learn Generative AI with PyTorchA training control method that stops fitting when validation or other monitored performance no longer improves.
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Building LLM Applications with DSPyEarly stopping is a technique that halts an evaluation or training process before its planned completion when further work is unnecessary or unproductive.
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Transformers in ActionEarly stopping is a training procedure that halts optimization when a validation measure stops improving for a specified interval.

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Related concepts

overfittingaddresses · Overfitting is a modeling condition in which a model fits patterns or idiosyncrasies in its training data too closely and consequently generalizes poorly to new data.
false positiveproduces · A false positive is a result that incorrectly indicates a condition or class is present when it is absent.
p-hackingkind of · P-hacking is manipulating analyses, data selection, testing procedures, stopping rules, or reporting choices to obtain apparently statistically significant results.
regularizationkind of · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.
validation setuses · A validation set is held-out data used to assess a model during development and guide model selection or tuning, rather than to fit its parameters.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.