Overfitting

As explained in Data Without Labels

Overfitting is a modeling condition in which a supervised learning model performs well on training data but less accurately on unseen data.

Overfitting occurs when a supervised learning model performs well on training data but less accurately on unseen data. It defeats the goal of generalizing to real-world datasets. section 3.2

High dimensionality can increase model complexity, sparsity, processing time, and overfitting risk. The Hughes phenomenon describes performance improving initially as variables increase, then declining after a point. section 3.11 section 3.2

Dimensionality reduction can reduce overfitting by:

• Removing insignificant or correlated variables that add little predictive value. section 3.11 section 3.7
• Using PCA or SVD to transform many variables into fewer components. section 3.11 section 3.7
• Reducing dataset complexity, which can improve model accuracy and shorten training time. section 3.7

Insurance customer data

An insurance dataset may combine useful variables—age, profession, policy amount, income, and defaults—with irrelevant details such as shoe color or shampoo brand. Including every variable increases complexity and can contribute to overfitting; reducing dimensions helps simplify the modeling problem. section 3.2

Figure 3.2 The Hughes phenomenon shows that the performance of a machine learning model will improve initially with an increase in the number of dimensions. But a further increase leads to a decrease in the model’s performance.

The Hughes phenomenon illustrates why adding variables can eventually reduce model performance, making dimensionality a practical overfitting concern. section 3.2

How Data Without Labels covers it

How deep it goes

Defines the condition, links it to dimensionality, and gives PCA/SVD as mitigations without detailing model diagnostics.

Examples

Uses insurance customer variables and the Hughes performance curve to illustrate irrelevant dimensions and declining model performance.

What it assumes

Assumes supervised learning and model-performance concepts while defining overfitting directly.

What you can do after

Apply PCA or SVD to reduce variables and mitigate overfitting, while recognizing possible information loss and reduced interpretability.

How it connects

Connects overfitting with supervised learning, the curse of dimensionality, the Hughes phenomenon, dimensionality reduction, PCA, and SVD.

liveBookmanning.com

Read Data Without Labels 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
  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
  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.
• 

  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 model-fitting technique that uses constraints or penalties to discourage overfitting or undesirable complexity and improve generalization.
• 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 control technique that terminates an iterative process before planned completion when a specified criterion indicates that further steps are unnecessary or unlikely to improve the objective.
• underfittingcontrasts with · Underfitting is a modeling failure in which a model lacks sufficient capacity or training to capture relevant patterns in data, resulting in poor performance, including 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 Data Without Labels. The section links point to the parts of the book it draws on.