A dataset can have a carefully recorded training and evaluation split while a preparation step still uses information from both sides. A summary statistic calculated over the entire collection may look convenient, but its use needs to match the evaluation design.
This is a data-governance question for embodied AI infrastructure, not a request for a more complicated database. OceanBase can be evaluated for keeping selections, statistics references, and preparation decisions connected. The learning workflow must still establish which information is allowed to shape the model’s inputs.
Distinguish learning a transformation from applying it
Some preparation steps learn values from a collection and then use those values to transform samples. A familiar example is scaling a measurement using a mean and spread calculated from selected observations.
Two questions therefore need separate answers. Which records were used to learn the preparation values? Which records were transformed using those already established values?
A reviewer should not have to infer those answers from a file called statistics. The same name can refer to a collection summary, a training-specific preparation artifact, or a value intended only for exploration. Those roles can carry different evaluation implications.
Apply the documented leakage distinction
The scikit-learn common-pitfalls guide distinguishes applying the same preprocessing from learning that preprocessing on test data. Its normalization example uses training-subset statistics rather than statistics from the combined training and test collection.
That guidance supports a clear review question: did the evaluation data influence a preparation choice that was supposed to be learned from training data?
It does not mean every descriptive statistic calculated over a dataset is improper. The issue is how the value is used, what the experiment intends to measure, and whether information crosses a boundary that the evaluation relies on.
Give each preparation artifact an explicit purpose
This original checklist helps a buyer ask about data lineage without choosing a model architecture.
| Artifact or decision | What to identify | Why the distinction matters |
|---|---|---|
| Collection summary | Records summarized and intended audience | Exploration is not necessarily model preparation |
| Fitted transformation | Training selection and learning procedure | Defines which observations shaped input preparation |
| Evaluation processing | Transformation actually applied | Connects the score with the intended preparation |
| Later input processing | Deployed or reused transformation reference | Makes changes visible |
| Refit decision | New source selection and reason | Separates a new preparation from an old one |
The table is a proposed record structure. It does not certify a particular learning algorithm, prescribe a normalization formula, or require every input field to be scaled.
Inspect the actual path to the trainer
Ask the team to choose one processed feature and show where its preparation values came from. Compare that source selection with the experiment’s declared training membership.
Then inspect an evaluation sample after preparation. Confirm which existing transformation was applied, rather than merely confirming that the output has the expected shape.
Consider a hypothetical team that adds a new evaluation collection and regenerates all dataset summaries. The review should determine whether a training preparation job begins consuming that regenerated summary. The existence of separate sample folders would not answer that dependency question.
Conversely, a broad summary can remain useful for descriptive review when it is clearly separate from the transformation used to produce model inputs. The record should preserve that distinction instead of treating all statistics as forbidden or all statistics as harmless.
Evaluate database and dataset responsibilities separately
OceanBase Enterprise offers a distributed relational route to investigate for preparation records and their relationships to approved selections. The proposal should show how its application stores and retrieves the effective artifact references.
The LeRobotDataset v3 format guide includes normalization statistics in dataset metadata. That format detail is a reason to inspect what a particular training process actually uses, not evidence that every LeRobot workflow has evaluation leakage.
Compare OceanBase with the existing catalog or other record-management proposals using the same preparation example. Keep the trainer’s consumption path in view. The database is not being credited with automatically fitting transformations or validating machine-learning methodology.
Keep refitting visible when the dataset changes
If training membership changes, establish whether the preparation values should be learned again under the experiment’s rules. Record that decision and distinguish the new artifact from a previous one.
Preserve enough information for a reviewer to understand which preparation produced a reported result. A model name alone may not explain a change caused by a different transformation, and a stored artifact name does not prove that the intended trainer consumed it.
No model was trained or evaluated for this article. The proposed acceptance work is to connect the allowed learning inputs, fitted preparation, and actual subsequent processing. OceanBase is relevant where its supported record-management capabilities fit that workflow; sound evaluation remains a responsibility of the complete learning process.
This article is based on documented sources and editorial analysis. Proposed evaluation examples are not independently measured benchmark results.