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[Lane 5 · docs] Precision/Recall/F1 — doc partials (math / intuition / examples / references)

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Valutazione

Difficoltà
3/5
Tempo stimato
Mezza giornata
Idoneità per principianti
72/100
Tipo di issue
Documentazione
Chiarezza
Specificata chiaramente
Stato di attività
Tranquilla
Stack tecnologico
kotlin, latex
Ambito
documentation

Direzione di ricerca

Start by reading docs/modules/ROOT/partials/ops/tensorops/matmul.adoc for the tag structure and the metrics how-to page for its existing example pattern. Create the three specified partials and include their tagged content in docs/modules/ROOT/pages/how-to/metrics-and-perf-testing.adoc; the examples tag depends on #1229. Build the site using the contributing guide or docs.yml workflow, and verify the math renders, the example compiles, and the reference links resolve.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

documentation good first issue size:s skill:docs sub-issue

Sub-issue of #1222 (Precision, Recall and F1Score metrics).

Lane: 5 · Docs / DARC
Skill needed: AsciiDoc and technical writing; enough ML background to explain precision vs. recall. No Kotlin beyond reading a 30-line class.
Size: s (~2–3 h for all three)
Blocked by: nothing for the math / intuition / references tags (start now); the examples tag needs #1229 so the example actually runs.

What to do

  1. Write three partials, one per metric, following the tag structure of
    docs/modules/ROOT/partials/ops/tensorops/matmul.adoc:
    • docs/modules/ROOT/partials/ops/metrics/precision.adoc
    • docs/modules/ROOT/partials/ops/metrics/recall.adoc
    • docs/modules/ROOT/partials/ops/metrics/f1score.adoc
      Each with non-placeholder // tag::math[], // tag::intuition[], // tag::examples[],
      // tag::references[] blocks. Use stem:[…] / [stem] (LaTeX; the playbook sets
      stem: latexmath) for the formulas. The math block must state the per-class
      definition, what macro vs. micro averaging does, and the zero-division convention
      adopted in #1224.
  2. Metrics are not TensorOps functions, so the operator-doc generator will not pick
    these partials up automatically. Surface them by adding a "Precision, recall and F1"
    subsection to docs/modules/ROOT/pages/how-to/metrics-and-perf-testing.adoc that
    include::partial$ops/metrics/f1score.adoc[tag=intuition] (and the others by tag).
  3. The examples block must be real code that compiles against develop after #1229 —
    ideally a tagged snippet in skainet-docs-samples included via include::example$…,
    like the existing TrainingDemo.kt accuracy example on that page; a plain
    [source,kotlin] block is acceptable if you say why.
  4. Build the site locally (see Contributing → Build from source / the docs.yml
    workflow) and check the math renders.

Acceptance

  • Three partials, all four tags non-empty, math renders
  • Metrics how-to page shows the new section with the intuition text and a runnable example
  • Every reference link resolves and supports the claim next to it (the reviewer in
    #1235 will click each one)

Notes

Intuition to convey: precision answers "of what I predicted positive, how much was right";
recall answers "of what was actually positive, how much did I catch"; F1 is their harmonic
mean, so it punishes a classifier that is high on one only because the other is near zero —
which is exactly where raw accuracy stays silent (predict the majority class everywhere:
high accuracy, near-zero minority recall). Also spell out that macro F1 is the mean of
per-class F1s, not the F1 of the macro means.

References to start from: sklearn precision_recall_fscore_support, torchmetrics
F1Score, and https://en.wikipedia.org/wiki/F-score .

Lingua principale
Kotlin
Stelle
52
Fork
15
Merge medio
1g 15h
PR unite (30g)
36

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

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