Overfitting

As explained in Causal Inference for Data Science

Overfitting is the condition in which a model captures noise or idiosyncrasies in training data and consequently generalizes poorly to new data.

Overfitting occurs when a model learns noise or peculiarities in training data so closely that it predicts new data poorly. Flexible function families can fit the past extremely well while failing to distinguish signal from noise. The central question is whether a model that predicts the past will also predict the future. section 4.1

Studying from solved exams

Memorizing every solved exam can produce excellent performance on familiar questions without demonstrating understanding. Setting some exams aside, studying the remainder, and testing on unseen exams estimates performance on the final exam. Train-test splitting applies the same idea: fit the model on train data and evaluate predictions against known outcomes in test data. section 4.1

Check for overfitting by separating ordinary model selection from prediction on unseen observations:

• Use cross-validation or train-test splitting to compare model configurations on data not used for fitting. section 4.1
• Do not assume cross-validation alone prevents overfitting when predictions are later made on the same dataset used for training. section 4.2.5
• Use cross-fitting when a causal procedure requires predictions for the observations used in the overall analysis: train on one fold and predict on another. section 4.2.5 section 4.6
• Select causal adjustment variables using domain knowledge rather than choosing every variable that improves predictive accuracy. section 5.3.2

Comparing models with varying complexity in data fitting. Although the model on the left fits the data more closely (it passes through all points), the model on the right can get the full picture better.

Compare a highly flexible curve that passes through every observed point with a simpler model that captures the broader pattern. section 4.2.5

In supervised learning, the target is the conditional expectation E[Y|X], not the individual observed value yi. Training on the same observations encourages predictions toward those observed values, especially with complex models. In causal inference, this distinction matters for S-learners, T-learners, propensity scores, and adjustment formulas. section 4.2.5 section 4.6

How Causal Inference for Data Science covers it

How deep it goes

Explains flexibility, noise, conditional expectation, train-test splitting, cross-validation, and cross-fitting, while stopping short of model-specific algorithms.

Examples

Uses a solved-exam analogy, train-test prediction, varying-complexity fits, license-plate recognition, and two-fold causal learners.

What it assumes

Assumes familiarity with supervised learning and introduces the distinction between observed outcomes and conditional expectations.

What you can do after

Diagnose overfitting with held-out evaluation and apply cross-fitting when causal predictions target observations used in analysis.

How it connects

Connects supervised learning, cross-validation, cross-fitting, causal inference, propensity scores, S-learners, and T-learners.

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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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  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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  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 Causal Inference for Data Science. The section links point to the parts of the book it draws on.