Overfitting

As explained in Graph Neural Networks in Action

A modeling failure mode in which a model fits training data patterns too closely and generalizes poorly to validation or unseen data.

Overfitting occurs when a model keeps improving on training data while its performance degrades on unseen validation data. In graph learning, diverging training and validation loss curves provide the clearest signal. section 3.1.4

Diagnose overfitting through a training workflow:

• Run training and validation loops for every epoch, recording both losses. section 3.1.3
• Plot the curves and identify the point where validation loss reaches its minimum before rising. section 3.1.4
• Retain the model instance with the lowest validation loss, then evaluate generalization on a separate test set. section 3.1.4 section 3.1.3

Graph model training

For the GCN and GraphSAGE models, validation loss begins diverging from training loss near epoch 20: training loss continues falling while validation loss rises. This indicates that additional training improves fit to the training graph data at the expense of generalization. section 3.1.4

Figure 3.1 Training and validation loss curves for the GCN model (left) and GraphSAGE model (right) trained in this section. The divergence of the validation from the training curve signals over-fitting, where the model learns the training data too well and at the expense of generalizing to new data.

Use the paired training and validation loss curves to locate divergence and the best validation-loss point. section 3.1.4

How Graph Neural Networks in Action covers it

How deep it goes

Explains curve divergence, validation-loss minima, and checkpoint selection, but defers corrective techniques to later treatment.

Examples

Uses GCN and GraphSAGE loss curves, epoch-20 divergence, validation checkpoints, and product-category classification metrics.

What it assumes

Assumes training, validation, test splits, loss tracking, and basic graph-model training workflows.

What you can do after

Diagnose overfitting with loss curves, select the lowest-validation-loss checkpoint, and assess generalization on test data.

How it connects

Connects overfitting with GCN, GraphSAGE, validation loss, model checkpoints, and generalization.

liveBookmanning.com

Read Graph Neural Networks in Action on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Grokking Machine LearningOverfitting is a modeling condition in which a model captures noise or idiosyncrasies in training data and generalizes poorly to new data.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Math and Architectures of Deep LearningA training failure mode in which a model fits idiosyncrasies of the training data too closely and generalizes poorly.
• 

  liveBookmanning.com
  Grokking AI Algorithms, Second EditionOverfitting is a condition in which a model matches training data too closely and performs poorly on varied unseen data.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  The AI Pocket BookA failure mode in which a model becomes too specialized to its training data and generalizes poorly to new cases.
• 

  liveBookmanning.com
  Learn Generative AI with PyTorchA training failure mode where the model memorizes training data too closely and generalizes poorly to new inputs.
• 

  liveBookmanning.com
  Data Without LabelsOverfitting is a modeling condition in which a supervised learning model performs well on training data but less accurately on unseen data.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Sutskever's ListOverfitting is a modeling failure in which a model fits training data too closely and performs poorly on unseen data.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  LLMs in ProductionOverfitting is a condition in which a model becomes excessively tailored to training data and performs poorly on unseen data.
• 

  liveBookmanning.com
  Reinforcement Learning from Human FeedbackOverfitting is a training condition in which a model fits its training data or objective too closely and generalizes poorly.
• 

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

  liveBookmanning.com
  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 Graph Neural Networks in Action. The section links point to the parts of the book it draws on.