# Evaluate Ml Pipeline

> Methodology for evaluating a single sklearn-compatible learner (in particular, the SkrubLearner produced by build-ml-pipeline). Owns: which entry point to call (skore.evaluate first, the explicit report classes when needed), which cross-validator to pick from scikit-learn's catalogue, how to consume the structural metadata (groups, times, …) attached at build time via .skb.markasX(splitkwargs=...). Stops at "what does the report say". Defaults (metrics, plots) come from skore; only override on explicit user request. TRIGGER when: code calls crossvalscore, crossvalidate, classificationreport, or any handwritten metric print (print(meansquarederror(...))); code calls .skb.crossvalidate(...) (route through skore for richer output); user asks how to score, evaluate, or compare a single learner; user asks how to pick a cross-validator; user wants to see a report / metrics / diagnostic plots for a fitted learner. SKIP when: declaring the pipeline (use build-ml-pipeline); hyperparameter / model search (separate skill); fitting, persisting, or serving the final model; tracking or comparing experiments across multiple runs over time (separate skill). HOW TO USE: invoke before any evaluation call. First, read the "Stop conditions" block at the top of the body and emit the Pre-flight checklist as visible text in your response — both are mandatory before any evaluation code is written. The structural facts about the data (group keys, time ordering) should already be encoded at the X marker via splitkwargs — if they aren't and you can't tell from the data, return to build-ml-pipeline and ask the user. For symbol-level lookups, defer to python-api (skore symbols) and python-api (splitters); don't guess names from memory.

## Facts
- Page: https://tashan.sh/capability/skill-probabl-ai-evaluate-ml-pipeline
- tashan id: skill:probabl-ai/evaluate-ml-pipeline
- Source: https://github.com/probabl-ai/skills
- Type: skill
- Category: other
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 97.0
- Freshness: 94.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: BSD-3-Clause
- Official: no

## Install

```sh
cp -r evaluate-ml-pipeline ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

---
Measured 2026-08-14 by tashan (https://tashan.sh) from public evidence. Scorer s5.