Early stopping

As explained in Transformers in Action

Early stopping is a training procedure that halts optimization when a validation measure stops improving for a specified interval.

How Transformers in Action covers it

How deep it goes

Defines the stopping rule and its generalization rationale without detailing implementation mechanics or metric selection.

Examples

Contrasts training performance with validation performance as additional epochs begin producing memorization.

What it assumes

Assumes familiarity with epochs, training, validation metrics, gradient descent, and overfitting.

What you can do after

Apply it by monitoring a validation metric and stopping training after improvement remains absent for a specified epoch interval.

How it connects

Connects epochs, validation metrics, overfitting, regularization, gradient descent, and generalization.

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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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  Machine Learning Algorithms in DepthEarly stopping is a training procedure that terminates optimization when performance on a validation criterion 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.

Compare how 9 books explain early stopping →

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 Transformers in Action. The section links point to the parts of the book it draws on.