Overfitting

As explained in Machine Learning System Design

A 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.

Overfitting occurs when a model performs well on training data but poorly on unseen data. It has become too specialized, capturing insignificant details rather than patterns that generalize. section 9.1.1

The problem reflects a bias–variance tradeoff. Increasing model capacity can capture more useful signals and reduce bias, but also increases sensitivity to training-set fluctuations and the risk of overfitting. section 9.1.1

Check these signals and contributors:

• Compare training and validation behavior rather than relying on training performance alone. section 9.1
• Treat decreasing training loss alongside increasing validation loss as a classic high-variance pattern. section 9.1.3
• Investigate excessive model complexity, data leakage, and noisy or highly granular features that encourage irrelevant patterns. section 9.1.1
• Reduce model capacity or increase regularization when the diagnostic pattern confirms overfitting. section 9.1.3

A diverging loss curve

A model’s training loss continues to decrease while its validation loss increases. The model is fitting the training examples more closely without improving on unseen data, indicating potential overfit. section 9.1.3

Figure 9.6 Loss is decreasing for training but not validation, reflecting potential overfit.

Use the loss-curve figure to recognize the visual pattern in which training loss decreases while validation loss does not. section 9.1.3

How Machine Learning System Design covers it

How deep it goes

Defines the condition, explains bias–variance causes, identifies contributing data problems, and prescribes capacity or regularization changes.

Examples

Uses training-versus-validation loss curves and increasing model complexity to illustrate overfitting and rising variance.

What it assumes

Explains training, unseen data, bias, variance, and underfitting alongside overfitting rather than assuming those distinctions.

What you can do after

Diagnose overfitting from learning or loss curves and respond by reducing model capacity or increasing regularization.

How it connects

Connects overfitting with learning curves, underfitting, bias–variance tradeoff, data leakage, model complexity, and regularization.

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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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  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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  Building Reliable AI SystemsA model-training outcome in which a model fits training examples too closely and generalizes poorly to new data.
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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.

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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 Machine Learning System Design. The section links point to the parts of the book it draws on.