Overfitting

As explained in Building Reliable AI Systems

A model-training outcome in which a model fits training examples too closely and generalizes poorly to new data.

How Building Reliable AI Systems covers it

How deep it goes

Recommends broad data coverage and balanced representation, without detailing model-level mechanisms.

Examples

Uses varied support issues, repeated customer questions, diverse conversation phrasings, and topics for style training.

What you can do after

Apply dataset-curation practices that broaden input coverage and limit overrepresentation of narrow subcategories.

How it connects

Connects overfitting to fine-tuning and training-data coverage.

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Also explained in

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  Grokking Machine LearningOverfitting is a modeling condition in which a model captures noise or idiosyncrasies in training data and generalizes poorly to new data.
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  Deep Learning with Python, Third EditionThe failure mode where performance improves on training data while degrading on unseen data because the model has adapted too specifically to the training set.
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  Deep Learning with R, Third EditionOverfitting is a modeling condition in which performance is better on training data than on previously unseen data, including when repeated tuning adapts a model to validation data.
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  Deep Learning with PyTorch, Second EditionA failure mode where a model memorizes irrelevant details of the training data and generalizes poorly, discussed here as a risk of increased capacity.
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  Causal Inference for Data ScienceOverfitting is the condition in which a model captures noise or idiosyncrasies in training data and consequently generalizes poorly to new data.
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  Natural Language Processing in Action, Second EditionOverfitting is a modeling failure in which a model relies too heavily on peculiarities of its training data and performs poorly on new data.
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  Math and Architectures of Deep LearningA training failure mode in which a model fits idiosyncrasies of the training data too closely and generalizes poorly.
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  Grokking AI Algorithms, Second EditionOverfitting is a condition in which a model matches training data too closely and performs poorly on varied unseen data.
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  Ensemble Methods for Machine LearningOverfitting is a modeling failure in which a model fits training data too closely and does not necessarily generalize well to new data.
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  The AI Pocket BookA failure mode in which a model becomes too specialized to its training data and generalizes poorly to new cases.
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  Learn Generative AI with PyTorchA training failure mode where the model memorizes training data too closely and generalizes poorly to new inputs.
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  Data Without LabelsOverfitting is a modeling condition in which a supervised learning model performs well on training data but less accurately on unseen data.
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  Investing for ProgrammersOverfitting is a modeling failure in which a model captures irrelevant variation in training features and fails to generalize reliably to new data.
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  Generative AI in ActionOverfitting is a modeling condition in which learned patterns fit training data too closely and fail to generalize well to new examples.
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  Machine Learning System DesignA condition in which a model fits its training data too closely and performs poorly on unseen data because it captures insignificant or non-generalizable patterns.
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  Starting Data Analytics with Generative AI and PythonOverfitting is a modeling failure in which a model fits noise or sample-specific variation rather than generalizable underlying patterns.
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  Demand Forecasting Best PracticesOverfitting is a modeling condition in which a model fits historical data well but fails to generalize to future or otherwise unseen data.
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  Sutskever's ListOverfitting is a modeling failure in which a model fits training data too closely and performs poorly on unseen data.
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  Statistics Every Programmer NeedsOverfitting is a modeling condition in which a model captures idiosyncrasies of the fitting data at the expense of performance on new data.
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  LLMs in ProductionOverfitting is a condition in which a model becomes excessively tailored to training data and performs poorly on unseen data.
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  Graph Neural Networks in ActionA modeling failure mode in which a model fits training data patterns too closely and generalizes poorly to validation or unseen data.
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  Reinforcement Learning from Human FeedbackOverfitting is a training condition in which a model fits its training data or objective too closely and generalizes poorly.
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  Financial AI in PracticeOverfitting occurs when a model captures patterns specific to its training data that do not generalize reliably to new data.

Compare how 24 books explain overfitting →

Related concepts

• regularizationaddressed by · 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.
• dropoutaddressed by · Dropout is a neural-network regularization technique that randomly suppresses units, connections, or activations during training to reduce overfitting.
• early stoppingaddressed by · Early stopping is a technique that terminates an iterative process before its planned completion when a specified condition indicates that further work is unnecessary or unlikely to improve the objective.
• underfittingcontrasts with · A modeling failure in which insufficient capacity or training prevents a model from capturing relevant patterns in the data, resulting in poor performance even on training data.
• failure modekind of · A failure mode is a distinct way in which a system, component, process, or operation can fail or produce an undesirable result.
• tree pruningaddressed by · Tree pruning is the removal of decision-tree branches that fail a specified split-quality condition.

This explainer was generated by Manning's knowledge graph from the text of Building Reliable AI Systems. The section links point to the parts of the book it draws on.