Ajwa or Medjool

Which date-fruit measurements support stable classification under a frozen train-only preprocessing plan?

After qualified ingestion and an immutable evaluation plan, recompute confusion matrices, classwise metrics and uncertainty from held-out predictions and frozen labels; fit preprocessing inside training partitions. Successful reproduction would not validate original measurements or causal interpretations.

What you could produce

• Exact input manifest with data-file digests and a schema/rights audit.
• Frozen evaluation protocol with split or group identifiers and an explicit baseline.
• Bounded analysis report with recomputable metrics, uncertainty and unresolved findings.

Before you use it

• Resolve exact data files and digests through qualified isolated ingestion.
• Audit data schema, provenance and license conditions before designing a bounded analysis.
• Freeze preprocessing, splits, metrics, runtime requirements and budget before execution.

Limits to keep in view

• No dataset contents, dependency compatibility, runtime cost or scientific result was checked.
• Grouping, timing or identifiers needed by the proposed protocol may be absent; report that limitation rather than inventing split labels.
• Large or specialized sources may require a predetermined subset or a broader qualified runtime; no resource fit is established.
• The qualified self-contained Python 3.13 pilot is not evidence that this dataset or its usual analysis packages can run.

Source and permission context

Attribute the supplied creators and UCI dataset DOI/source. Preserve license links and identify changes before any permitted data reuse.

License evidence recorded. Review the scope and upstream conditions before reuse.

Still unresolved

• Data files and embedded or third-party notices were not downloaded or inspected.
• The API's last-updated date is metadata, not an immutable dataset version or content digest.