docs: check whether a donor score is calibrated, by decile, on your own file

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Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
70/100
Tipo de issue
Documentación
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
python, scikit-learn
Área
documentation, machine-learning

Línea de trabajo

Read docs/how-to/estimate_donor_lifetime_value.md for the held-out-year pattern and philanthropy/datasets/_panel.py for make_donor_panel. Write the new page docs/how-to/check_score_calibration.md, then add it to docs/how-to/index.md and mkdocs.yml. Done when the decile table runs without a download and mkdocs build --strict passes.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

documentation good first issue roadmap
Goal

A user can tell whether "a score of 0.3" means about 30% of those donors give, using only scikit-learn, before they put the number in front of a gift officer.

Acceptance criteria
  • New how-to docs/how-to/check_score_calibration.md: fit DonorPropensityModel on the earlier fiscal years of a gift panel, score the last year, and compare predicted vs actual per decile with sklearn.calibration.calibration_curve(y, p, n_bins=10, strategy="quantile").
  • It shows the table, not only a plot, and says in plain words what an over- or under-confident decile means for a mailing list.
  • It shows CalibratedClassifierCV(..., method="sigmoid") as the fix and fits it on a fold the model was not trained on. The project's floor is scikit-learn 1.6, where cv="prefit" is deprecated: wrap the already-fitted model as CalibratedClassifierCV(FrozenEstimator(model), method="sigmoid") (from sklearn.frozen import FrozenEstimator).
  • It says what the library already does, so nobody calibrates twice: MajorGiftClassifier is Platt-calibrated internally over 5 folds, and the DonorPropensityModel docstring reports that an isotonic wrapper lowered the top-10% hit rate on all five KDD Cup 1998 seeds. The point of the page is checking calibration on a later year of your own file, not re-fitting it by default.
  • Runs on philanthropy.datasets.make_donor_panel so it needs no download; added to docs/how-to/index.md and mkdocs.yml; mkdocs build --strict passes.
Scope

Docs only. No new public function: scikit-learn already has calibration_curve and CalibratedClassifierCV.

Where to start
  • docs/how-to/estimate_donor_lifetime_value.md for the held-out-year pattern
  • philanthropy/datasets/_panel.py
Dependencies
  • None

Phase order is a release plan, not a rule against picking up independent work early. Comment on this issue before you start so two people don't build the same thing.

Lenguaje dominante
Python
Estrellas
11
Forks
23
Merge medio
1 d 6 h
PR fusionados (30 d)
35

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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