Plain-Language Communication of ML: Explain Training–Validation–Test Splits in Plain English for Regulators

1) Set the frame and definitions in plain English

When building a machine-learning (ML) model, we do not train it and judge it on the same data. Doing so would give an overly optimistic view of its accuracy and safety. Instead, we split our available data into three non-overlapping parts, and each part has a different job:

• Training set = learning. The model looks at this data to learn patterns. It adjusts its internal settings based on examples and known outcomes.
• Validation set = tuning. We use this separate slice to choose settings (sometimes called “hyperparameters”) and decide which version of the model works best. The model does not learn from this set; we only use it to measure and compare options during development.
• Test set = final check. After we finish building and tuning, we take a final, one-time measurement of performance on this held-out data. We do not look at this data during training or tuning. It is used once to understand how the model might perform in the real world.

Think of these sets as three separate, non-overlapping slices of the same overall data. They should not mix. If the same record appears in more than one slice, the final evaluation becomes unreliable. The core purpose of the split is simple: ensure that what looks good in development will hold up in reality. The separation helps us avoid fooling ourselves with inflated results and gives stakeholders a clearer view of true performance.

These three sets also support auditability and trust. By documenting how we split and used the data, we show that the model was not “coached” on the final exam. Regulators should be able to trace how the data flowed from raw inputs to training, to validation decisions, to the final test. This paper trail matters as much as the accuracy itself, because it demonstrates control, discipline, and integrity in the development process.

In many regulated contexts—finance, healthcare, public services—the stakes are high. A small bias or a small leak can become a large risk. The training–validation–test split is a basic control to reduce this risk. It is not a silver bullet, but it is a foundational safeguard that every ML project should implement and be able to explain in plain language.

2) Use a simple metaphor and minimal visuals to anchor understanding

A useful way to picture the three-way split is to think of a student preparing for an important, sealed final exam:

• Training = studying with textbooks and practice questions. The student learns methods and facts. Mistakes during practice are part of learning; the student adjusts and improves.
• Validation = mock exams to fine-tune strategy. The student takes timed practice tests to decide on tactics: which method is faster, what to attempt first, and when to double-check. The goal is not to learn new material but to optimize strategy.
• Test = the sealed final exam, taken once. The final exam is unopened until exam day. The student must not see it earlier. Performance here shows what the student truly knows and how well the strategy generalizes to new problems.

To visualize with minimal structure, imagine three labeled boxes in a row:

• Box 1: Training (learn)
• Box 2: Validation (tune)
• Box 3: Test (final check)

Arrows point from Training to Validation to Test, indicating the development flow. Data flows into these boxes once. Results flow out for decision-making. Critically, we do not draw arrows that send records back and forth between boxes. Each box remains clean and separate.

This mental picture prevents common mistakes. If we treat the test set like another mock exam we revisit many times, we slowly “study” the test and inflate the score. If we accidentally copy items from Training into Validation or Test, we risk hidden leakage. The boxes help everyone—from engineers to executives—keep the boundaries in mind.

3) Tie to regulatory risks and required evidence

From a regulatory perspective, the purpose of the split is to reduce specific, predictable risks. We highlight three core risks and the evidence needed to address them:

• Leakage (overlap between sets).

  • Risk: If the same individual, transaction, or event appears in more than one set, the model may indirectly “memorize” and appear better than it really is. Subtle forms of leakage happen when there is shared identity, shared time windows, or derived features that leak future information.
  • Evidence to provide: A documented methodology that prevents overlap. For example, grouping by user or account and assigning all records for that group to a single set; deduplicating by unique IDs; checking that no IDs or near-duplicates appear across sets. Logs or SQL statements that show how the split was enforced.

• Non-representative splits (unfair or fragile performance).

  • Risk: If one set has a different profile than real-world data—different time period, geography, demographics, or rare-case ratio—the measured performance may not hold. The model may underperform for certain subgroups.
  • Evidence to provide: A comparison of distributions across sets and against production data. This includes class balance (e.g., proportion of positives to negatives), key demographic or geographic variables, critical risk factors, and time periods. Provide summary tables or simple histograms that show similarity and stability. Explain any differences and how they were mitigated (e.g., stratified sampling, time-based splits, or reweighting).

• Repeated peeking at the test set (inflated claims).

  • Risk: If developers keep checking the test set while tuning the model, they slowly fit to that set. This inflates the final score and undermines credibility. It is like opening the sealed exam during study.
  • Evidence to provide: A protocol stating that the test set remained locked until final evaluation, with access controls and audit logs. If multiple models were tried, show that selection was based on validation performance only. Include the date and hash of the test dataset, who had access, and when it was used.

In addition to addressing these three risks, regulators expect evidence that the process is deliberate and repeatable. The following items help build that assurance:

• Documented split method. Describe in plain English how data was partitioned. State the rationale: for example, “We used a time-ordered split to avoid training on future information.”
• Time-order handling. If data changes over time, explain how you prevented future leakage. A common practice is to train on earlier periods, validate on a subsequent period, and test on the most recent period that remains unseen.
• Class balance checks. Show that the proportion of key outcomes (such as fraud/no fraud) is similar across splits, or explain why it differs and how you adjusted.
• Subgroup stability. Report performance not only overall but also across relevant subgroups (e.g., region, age bands, product lines), with confidence intervals when possible. This helps detect fairness, safety, or stability issues.
• Performance variance and robustness. If you used multiple random seeds or repeated splits, summarize the range of outcomes. Stability across runs suggests the model is less sensitive to chance.
• Simple visuals and plain labels. Prefer easily read bar charts or line plots over complex figures. Label axes and metrics in plain English, e.g., “Share of correct alerts out of all alerts.”

Remember that for regulators, clarity and traceability can be as important as accuracy. Show not just the results, but how you got them, in a way a non-technical reader can verify.

4) Provide templates and before–after rewrites that the learner can adapt

To ease communication with regulators and executives, here are reusable, plain-language templates. You can copy and adapt them to your context.

• Data split overview (template): “We divided our dataset into three non-overlapping parts: a training set used for the model to learn patterns, a validation set used to tune and compare models, and a test set used once at the end for a final, unbiased check. Records do not appear in more than one set.”

• Method and rationale (template): “We used a time-ordered split: data from January–September formed the training set, October was used for validation, and November was kept aside as the test set. This prevents the model from learning from future information and supports a realistic evaluation.”

• Leakage controls (template): “To prevent overlap, we assigned all records for each customer ID to a single set. We confirmed that no customer IDs appear in more than one set. We also removed duplicate events and features that reveal future outcomes.”

• Class balance and representativeness (template): “The outcome rate is consistent across splits: 3.1% in training, 3.0% in validation, and 3.2% in test. Key demographics and regions show similar proportions. Any differences under 1 percentage point are noted and monitored.”

• Access control for the test set (template): “The test set was locked until the end of development. Only two team members had access for final scoring. Access was logged, and the dataset was hashed on creation (SHA-256: [hash]) to verify it was not modified.”

• Subgroup performance reporting (template): “We report results overall and for key subgroups (e.g., region, age bands). Performance is within ±2 percentage points across subgroups. We will monitor these gaps in production and retrain if gaps widen.”

• Decision-use statement (template): “The model’s test-set performance meets our threshold for deployment. We will use it to prioritize reviews, not to make final decisions without human oversight. We will re-evaluate quarterly using a fresh test set.”

• Change control (template): “Any future model updates will repeat the same split procedure, with a new, untouched test set. We will document differences in data, performance, and subgroup stability for audit.”

Now, let’s convert jargon-heavy phrasing into clear regulatory language. These before–after rewrites align with decision-making needs and avoid unnecessary technical terms.

• Before (jargon): “We did an 80/10/10 random split and tuned hyperparameters based on cross-validated AUC. Final model selection used the highest test AUC.”

  • After (plain English): “We split the data into three separate parts. The largest part taught the model. A second part helped us choose settings. We saved the final part for a one-time, independent check. We chose the model based on results from the tuning step only, and we looked at the final check once at the end.”

• Before (jargon): “We performed stratified sampling across the positive class to control base-rate variance and used target leakage audits on feature sets.”

  • After (plain English): “We kept the proportion of key outcomes similar across the data splits so that results are comparable. We also checked that no feature gives away future information or the correct answer by accident.”

• Before (jargon): “Temporal validation was conducted with forward chaining to mitigate lookahead bias.”

  • After (plain English): “We trained on earlier months and validated on later months to make sure the model does not learn from the future. This mirrors how the model will work in real use.”

• Before (jargon): “We iterated on the leaderboard until the public test metric converged.”

  • After (plain English): “We avoided repeatedly checking the final test results. We made all tuning decisions using the validation set. We ran the final test once, at the end.”

• Before (jargon): “We observed heterogeneous performance across demographic cohorts with sensitivity to class imbalance.”

  • After (plain English): “We checked results for different groups. Some groups performed differently. We adjusted our sampling and re-evaluated. We will keep monitoring these gaps after launch.”

• Before (jargon): “We used nested cross-validation for hyperparameter optimization and unbiased generalization error estimation.”

  • After (plain English): “We used a careful two-level process: one level to choose settings and another to measure final performance without mixing the two. This keeps the final check independent.”

• Before (jargon): “We implemented deterministic data partitioning with salted hashing of identifiers.”

  • After (plain English): “We used a repeatable rule to assign each record to one data slice only. The same record will always go to the same slice if we rerun the process.”

These rewrites keep the meaning but make the intent and controls obvious to non-technical readers. They help regulators focus on the core questions: Is the process disciplined? Are the results likely to hold in the real world? Are we managing risks to groups and the public?

Bringing it together

The training–validation–test split is a simple structure with a serious purpose. Each slice has a clear job: the training set teaches the model, the validation set helps us tune and choose, and the test set provides a single, independent check at the end. Keeping these sets separate protects against inflated performance and helps ensure that the model’s claims are trustworthy.

For regulated environments, the split also provides a backbone for documentation and oversight. By preventing leakage, ensuring representativeness, and avoiding peeking at the final test, we improve reliability. By documenting split methods, handling time-order, checking class balance, and reporting subgroup stability, we provide the evidence that regulators expect. And by using plain-English templates and clear rewrites, we make the process auditable for executives and regulators who are accountable for outcomes but are not ML specialists.

Use this approach consistently. Explain the split in simple terms. Tie every claim to a control or a check. Present numbers and simple visuals that anyone can read. Above all, keep the test set sealed until the end. This disciplined, transparent process is the foundation for ML systems that are not only accurate, but also explainable, compliant, and worthy of public trust.