## TL;DR
Fix this in Labelbox by acting on the exact message first: attach predictions to the model run data rows first, confirm a sample row shows a prediction, and only then start the evaluation. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

```text
model run has no predictions to evaluate
```

## Use this when
- The operation in Labelbox fails with this exact message: model run has no predictions to evaluate.
- The same failure repeats on retry with unchanged inputs.
- A review, export or check is blocked until this item completes.

## Not for this skill when
- The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
- The operation completes and you only disagree with its output. That is a quality review, not this failure.

## Steps
1. Reproduce the failure with the smallest scope that still shows it in Labelbox, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: attach predictions to the model run data rows first, confirm a sample row shows a prediction, and only then start the evaluation. Verify: the run lists predictions on its rows and evaluation produces metrics.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in Labelbox on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, decisions or alerts against the source in Labelbox so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

## Why it happens
Evaluation compares predictions against labels; a run created from data rows alone has nothing to score.

## Variant phrasings

### model run has no predictions to evaluate
The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

### How to fix: Labelbox model run failed error "model run has no predictions to evaluate"
The how-to phrasing for the same failure in Labelbox. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

### Labelbox troubleshooting for this failure
The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: Labelbox at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_nGtT8MZphs7vDncZ6QoPJA