Data leakage

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Information leakage, or data leakage, is the use or transfer of predictive information unavailable at prediction time, producing overly optimistic evaluation results.

Data leakage occurs when a model uses predictive information that will not be available when a real prediction is made. It can temporarily inflate training or validation results, then lead to disappointing performance on new cases. The practical test is temporal consistency: features should describe the prediction point or an earlier point, not information created afterward. section 2.2.7

Leakage appears at two levels. Feature-level leakage uses an unavailable column directly. Row-level leakage arises when observations are connected by time or group membership, allowing a model to exploit relationships that will not recur in validation or deployment. section 2.1.1 section 2.2.7

Prevent misleading estimates by checking both features and splits:

• Verify when every feature and target is generated; exclude a feature when it is produced after the prediction point. section 2.2.7
• Represent meaningful group membership when appropriate, but segregate groups across cross-validation folds when that membership cannot be reproduced in test data. section 2.1.1
• Treat leakage features as a tabular-data pathology alongside irrelevant, duplicated, highly collinear, or poorly populated columns. section 2.2 section 2.5
• Exercise a trained model on a few genuinely new data points early, forcing an explicit check of which inputs will exist after deployment. section 9.5.1 section 9.6

Loan decisions and future behavior

For a loan-granting model, late-payment history can look highly predictive in historical training data. Yet it is future behavior at the time the loan decision is made, so using it creates future leakage rather than a usable production feature. section 2.2.7

Property-price predictions

A model predicting whether a new Kuala Lumpur listing will sell above or below the median cannot rely on “Weeks on the market,” because that value is known only after the property has sold. Trying a brand-new listing exposes the missing input immediately. section 9.5.1

Figure 9.18 Validating the features used to train the model with a new data point

Use the figure to trace how validating a feature against a new listing reveals whether it exists at prediction time. section 9.5.1

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes

Defines row- and feature-level leakage, gives temporal and group-splitting remedies, and demonstrates a deployment-oriented check.

Examples

Uses loan repayment behavior and Kuala Lumpur property listings with weeks-on-market as unavailable future information.

What you can do after

Diagnose/debug with it by auditing feature timing, isolating groups in validation, and testing trained models on new cases.

How it connects

Connects leakage to cross-validation, non-IID data, time series, group membership, feature engineering, and deployment.

liveBookmanning.com

Read Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  LLMs in ProductionData leakage is the inclusion of evaluation or otherwise unavailable information in a model's training data or training process.
• 

  liveBookmanning.com
  Inside Deep Learning: Math, Algorithms, ModelsA data-preparation failure in which label-related information incorrectly appears in features and inflates apparent model performance.
• 

  liveBookmanning.com
  Deep Learning with R, Third EditionAn information leak is unintended transfer of information from evaluation data into a model or its development decisions.
• 

  liveBookmanning.com
  Machine Learning Platform EngineeringData leakage is the unintended inclusion or reuse of information in a dataset that compromises valid model development or evaluation.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionThe unintended transfer of information from evaluation data into model choices, causing biased estimates of generalization.

Compare how 9 books explain data leakage →

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.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.