How deeply does Deep Learning with Python, Third Edition cover data leakage?

Explains leakage through repeated hyperparameter tuning and stops short of broader leakage mechanisms.

Data leakage

As explained in Deep Learning with Python, Third Edition

The unintended transfer of information from evaluation data into model choices, causing biased estimates of generalization.

Data leakage occurs when evaluation information influences model choices, making validation or test performance look better than performance on genuinely new data. section 5.2.1

Repeated validation tuning

Try a configuration, evaluate it on the validation set, and change the model based on the score. Repeating this process leaks increasing information about the validation data and can overfit the configuration to it. section 5.2.1

Use separate evaluation roles:

Tune model configuration with training and validation data, recognizing that repeated validation feedback becomes a form of learning. section 5.2.1
Reserve the test set as a completely different, never-before-seen dataset. section 5.2.1
Use hold-out validation, K-fold validation, or iterated K-fold validation when data is limited. section 5.2.1

How Deep Learning with Python, Third Edition covers it

Cover of Deep Learning with Python, Third Edition

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Also explained in

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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIInformation leakage, or data leakage, is the use or transfer of predictive information unavailable at prediction time, producing overly optimistic evaluation results.
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Graph Algorithms for Data ScienceData leakage is the incorporation of information into model features or training that would not be available when making the intended prediction.
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Ensemble Methods for Machine LearningData leakage is the unintended use of information during model training or preprocessing that would not be available when making the intended predictions.
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Delivering Machine Learning Projects: From design to deploymentData leakage is the unintended use or transfer of information from evaluation data or future observations into model training or selection, causing misleadingly optimistic performance estimates.
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LLMs in ProductionData leakage is the inclusion of evaluation or otherwise unavailable information in a model's training data or training process.
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Inside Deep Learning: Math, Algorithms, ModelsA data-preparation failure in which label-related information incorrectly appears in features and inflates apparent model performance.
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Deep Learning with R, Third EditionAn information leak is unintended transfer of information from evaluation data into a model or its development decisions.
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Machine Learning Platform EngineeringData leakage is the unintended inclusion or reuse of information in a dataset that compromises valid model development or evaluation.

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Related concepts

dataset splitaddressed by · A dataset split is the division of a dataset into distinct subsets assigned different purposes, such as training, validation, or testing.
defense in depthaddressed by · Defense in depth is a security strategy that combines multiple protective controls so that the failure or compromise of one does not eliminate all protection.
state transitioncontrasts with · A state transition is a change from one modeled state of a system, object, or process to another according to an event, action, condition, or rule.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with Python, Third Edition. The section links point to the parts of the book it draws on.