Episode 86 — Data Leakage: “Too Good to Be True” Results and How to Catch Them
In Episode eighty six, titled “Data Leakage: ‘Too Good to Be True’ Results and How to Catch Them,” we focus on one of the most common reasons a model looks brilliant in development and then collapses in the real world. Leakage is not a minor technicality, because it does not merely bias results a little, it can completely invalidate your evaluation and mislead decision makers into deploying a system that never had a chance. The tricky part is that leakage often produces exactly what teams want to see: unusually high scores, smooth curves, and confident claims of success. That makes it psychologically dangerous, because the more impressive the result, the less likely someone is to challenge it. The goal of this episode is to make “too good to be true” feel like a warning siren, not a victory lap, and to give you reliable ways to detect leakage before it becomes expensive.
Before we continue, a quick note: this audio course is a companion to the Data X books. The first book is about the exam and provides detailed information on how to pass it best. The second book is a Kindle-only eBook that contains 1,000 flashcards that can be used on your mobile device or Kindle. Check them both out at Cyber Author dot me, in the Bare Metal Study Guides Series.
Leakage can be defined as using information during training or evaluation that would not be available at the time the prediction is supposed to be made. This often includes future information, target information, or proxy signals that are created as a consequence of the outcome you are trying to predict. In other words, the model is allowed to peek at the answer key, even if that peek is indirect and unintentional. Leakage can occur through features, through the way the data is split, through preprocessing steps, or through the way labels are constructed. The critical idea is that leakage breaks the causal direction of prediction, because it allows the model to learn patterns that rely on information that arrives after the decision point. When you deploy, that information is not present, so the performance you measured cannot be reproduced in the environment that actually matters.
One of the most practical ways to catch leakage is to learn to spot “leakage fields,” meaning columns that are suspicious because of what they represent rather than because of their name alone. Identifiers are a classic example, because a user identifier, device identifier, or transaction identifier can act like a lookup key that lets the model memorize outcomes for entities it has already seen. Timestamps can be leaky when they encode a sequence in which later events correlate strongly with the outcome, especially if the split allows future records to appear in training. Post outcome attributes are another major warning sign, such as a field that indicates resolution status, refund processed, account closed, or incident ticket created, which may only exist after the event has occurred. Even when these fields feel “real,” they are not legitimate predictors if they are downstream of the label. The seasoned habit is to ask whether a feature would exist at prediction time and, if not, treat it as guilty until proven innocent.
Preprocessing leakage is a quieter but equally damaging form of cheating, and it often happens when transformations are fit on the full dataset instead of being fit only on training data. Scaling, normalization, imputation, and encoding steps typically compute statistics such as means, standard deviations, medians, or category mappings. If you compute those statistics using both training and evaluation data together, the evaluation data influences the feature space the model learns, which inflates performance. This is especially subtle because it can happen even when you never touch the labels of the evaluation set, making it feel harmless. The correct approach is that each split or fold should treat preprocessing as part of the training pipeline, meaning it is fit on training only and then applied to validation or test. Once you think of preprocessing as “learning,” the rule becomes obvious, because learning from evaluation data is leakage even when it happens through statistics rather than through labels.
Label leakage deserves special attention because it can arise from how the target is defined, not just from the features. Label leakage occurs when the label is constructed using information that also appears in features or when the label itself includes derived signals that are available only after the outcome. For example, a label defined by whether a case triggered a specific downstream process can be leaky if that downstream process is also represented in the features. Another form is when the label is derived from aggregates that include future behavior, such as defining churn based on a long window that overlaps with the period in which you are supposedly making predictions. The danger is that the model learns to detect the label construction mechanism rather than the underlying phenomenon you intended to predict. In an exam context, label leakage is often tested through scenario descriptions where the label definition sounds plausible but quietly violates the prediction timeline. If the label encodes the answer through a downstream artifact, the model is not predicting, it is detecting a breadcrumb left by the outcome.
Proper splitting is one of your strongest defenses against leakage, but it must match the structure of the problem rather than relying on a generic random split. Time splits are essential when the decision is forward looking, because they prevent training from seeing future patterns that would not exist at prediction time. Entity splits are necessary when multiple records belong to the same person, device, account, or organization, because otherwise the model can memorize entity specific patterns and then appear to generalize when it is actually just recognizing the same entity again. In many operational datasets, the unit of independence is not the row but the entity, and splitting by row leaks information through repeated exposure. The point is that you must split in a way that preserves the independence of evaluation examples from training examples at the level that matters. If you do not, your evaluation becomes a rehearsal with the same actors rather than a test with new ones.
Reading scenario clues is a skill, and it becomes much easier when you train yourself to look for temporal or procedural phrases that imply downstream information. Clues include language like “after investigation,” “once the case was resolved,” “refund issued,” “ticket created,” “account status updated,” or “final diagnosis,” because these are often outcomes of a process that happens after the event. Another clue is the presence of aggregated historical features that might accidentally include the target window, such as “number of incidents in the last thirty days” when the prediction is supposed to occur at the start of that same window. In security contexts, watch for features derived from incident response artifacts, because those artifacts are usually created in reaction to the very events you are trying to predict. If a scenario claims extremely high accuracy on a challenging problem with sparse positives, treat that as a clue too, because leakage often expresses itself as implausible performance. Scenario reading is about reconciling the stated prediction time with what the features logically require.
A reliable way to confirm leakage is to retrain using a strict pipeline with hard boundaries and then check whether performance drops in a way that suggests the original result was inflated. This is not about hoping the score falls, but about treating the pipeline as a controlled experiment: remove suspect features, enforce correct preprocessing boundaries, and apply appropriate splits. If the model’s performance collapses dramatically, that is often evidence that the original model relied on information it should not have had. A performance drop does not automatically prove leakage, because the strict pipeline may also remove legitimately predictive signal, but a sudden fall from near perfect to mediocre is a strong warning. The important discipline is that you do not accept a great score until it survives the strict version of the process. This approach also gives you a practical debugging pathway, because you can reintroduce features or steps gradually to identify exactly what created the inflation.
Feature selection is another place where leakage sneaks in, particularly when selection is performed using the full dataset, including test data, to compute correlations, statistical tests, or feature importance. Even if you never train on the test labels directly, using test labels to decide which features to keep is still learning from the test set. This produces an evaluation that is biased toward features that happened to correlate with the label in that particular test set, which may not hold in future data. Feature selection must be treated like any other learning step, meaning it is performed within the training data only, ideally within each cross validation fold when you are evaluating. The exam often frames this as “peeking” at the test set to pick the best features, and that language is accurate because it is effectively reading the answer key to decide what to study. If selection used test data, your final score is no longer an unbiased estimate.
Holdout sets and reproducible pipelines are the structural controls that prevent accidental leakage from creeping in as projects evolve. A holdout set remains untouched until the end, and its value comes from the fact that you cannot optimize to it if you never look at it during development. Reproducible pipelines enforce that preprocessing, feature selection, and training happen in a consistent order with consistent boundaries, reducing the chance that an ad hoc shortcut slips into the workflow. When pipelines are reproducible, it becomes easier to audit what was done and to spot where information might have crossed a boundary. This is especially important in teams where multiple people touch the data, because leakage is often introduced by well intentioned improvements like “cleaning the data” in ways that accidentally use future information. Prevention is therefore less about one brilliant test and more about building a process that makes cheating difficult even by accident.
When you communicate leakage, it is important to frame it as a process flaw rather than as a weakness of the model family, because the model is behaving exactly as it is designed to behave. Learning algorithms will exploit any predictive signal you provide, whether that signal is legitimate or leaky, because they are not moral agents and they do not know what is fair. If the pipeline allows leakage, a strong model will find it quickly, and the resulting score is a measurement of pipeline contamination, not model capability. Communicating leakage as a process issue protects teams from the wrong reaction, which is to blame the algorithm or to swap model types while leaving the leak intact. It also helps stakeholders understand why a dramatic drop after fixing leakage is not a failure, but a return to honest measurement. The professionalism here is acknowledging that the previous result was not real, and that the corrected process gives you a trustworthy baseline to improve from.
Documentation is the final guardrail that keeps leakage prevented over time, because it preserves the rationale for exclusions and split logic even after the original developers move on. Documenting exclusions means writing down which fields were removed and why they were considered post outcome, proxy, or otherwise unavailable at prediction time. Documenting split logic means describing whether the split is time based, entity based, or both, and what prediction timestamp defines the boundary. This prevents a future iteration from accidentally reintroducing leaky fields or changing the split in a way that looks harmless but reopens the leak. Documentation also supports audits and incident reviews, because leakage can be a root cause of operational failures. In regulated or high stakes environments, being able to explain and defend your evaluation design is as important as the model itself.
The anchor memory for Episode eighty six is blunt for a reason: if a feature knows the answer, it is cheating. A feature can “know the answer” directly, such as a post outcome status flag, or indirectly, such as an identifier that allows memorization or a preprocessing step that bakes evaluation statistics into training. This anchor helps you quickly judge suspiciously strong results, because it prompts you to ask whether the information pathway is legitimate at prediction time. It also reminds you that leakage is not always obvious, because cheating can happen through timing, aggregation windows, or the use of evaluation data in selection steps. When you keep this anchor in mind, you stop treating model performance as a mystery and start treating it as evidence that must be explained. In practice, the best teams celebrate strong results only after they have proven the pipeline is clean.
To conclude Episode eighty six, titled “Data Leakage: ‘Too Good to Be True’ Results and How to Catch Them,” you should be able to name three leakage sources and then state the prevention step you will apply. Three common sources are post outcome feature fields that are created after the event, preprocessing fitted on the full dataset instead of training only, and improper splitting that allows the same entity or future records to appear in both training and evaluation. A disciplined prevention step is to enforce a reproducible pipeline where splitting happens first using time or entity logic as appropriate, then all learning steps including preprocessing and feature selection are fit only on training data within that split, and finally evaluation is performed on untouched holdout data. That prevention step matters because it converts leakage control from a one time detective effort into a repeatable process constraint. When you can articulate those sources and that prevention step clearly, you demonstrate the core competency: treating evaluation integrity as non negotiable so real world performance has a chance to match what you measured.
