How deeply does Machine Learning Platform Engineering cover data leakage?

Explains leakage through holdout curation, split changes, and sample-collection controls, but does not detail specific leakage algorithms.

Data leakage

As explained in Machine Learning Platform Engineering

Data leakage is the unintended inclusion or reuse of information in a dataset that compromises valid model development or evaluation.

Data leakage is the unintended inclusion or reuse of information that compromises valid model development or evaluation. In production ML, leakage can invalidate performance estimates, make results irreproducible, and trigger unnecessary retraining when dataset splits change. section 2.1.4 section 2.1.1

Control leakage by making datasets and evaluation processes explicit, traceable, and repeatable:

Curate a separate holdout dataset whose diversity, distribution, and characteristics match production data. section 2.1.4
Treat training, test, and holdout splits as versioned assets; changing a split requires reevaluation and retraining. section 2.1.4
Track raw data, intermediate datasets, and augmented or annotated versions so problematic samples can be identified and corrected. section 2.1.1

Operational check

Before comparing model versions, verify that the holdout data remains separate, representative, versioned, and unchanged without documented reevaluation. If the split changes, restart the evaluation and retraining loop. section 2.1.4

How Machine Learning Platform Engineering covers it

Cover of Machine Learning Platform Engineering

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

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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.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Platform Engineering. The section links point to the parts of the book it draws on.