[Feature request] Official feature-attribution support for KumoTabular, inspired by TabICL's interpretability API

Aperta
  • Qualcosa non va?
#1,276 1 commento 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 1 giorno

Nessuno ha ancora preso questa issue.

Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
30/100
Tipo di issue
Funzionalità
Chiarezza
Abbastanza chiara
Stato di attività
Attiva
Stack tecnologico
python
Ambito
machine-learning

Direzione di ricerca

Start by reading the existing sdm.explain GradientExplainer API and its documentation to understand the current outputs and limitations. Then compare its behavior with the linked TabICL SHAP implementation and the exploratory setups described here. Done would mean maintainers identify a recommended attribution workflow or define a supported API and document its baseline, masking, ensemble, and reconstruction semantics.

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

Descrizione

Motivation

Thank you for releasing KumoTabular!

I am evaluating it against CatBoost and TabICL on a binary classification
task. Beyond predictive performance, I would like to understand individual
predictions: which features increase or decrease the predicted probability
for a selected class?

An officially supported and documented feature-attribution workflow would
help users evaluate and adopt the model.

Reference: TabICL's interpretability tools

The standalone TabICL package provides helpers such as
tabicl.shap.get_shap_values and get_shap_explainer, returning standard
SHAP explanations that can be used for local and aggregate visualizations.

Its current implementation uses an all-NaN background as a missing-value
reference, handled through the model's preprocessing:

https://github.com/soda-inria/tabicl/blob/main/src/tabicl/shap/_shap.py

A similarly accessible workflow for KumoTabular would be very useful,
without necessarily using the same attribution method or masking strategy.

Existing functionality and questions

I noticed that sdm.explain already exposes GradientExplainer.
Could you clarify whether it is the recommended explanation method for
KumoTabular, what its outputs represent, and its limitations?

For additive feature attributions, would you recommend:

  • SHAP with representative background rows;
  • SHAP with an all-missing reference;
  • or another model-specific approach?

I understand that Kumo encodes missingness explicitly, so masking with NaN
should not be assumed equivalent to removing a feature.

I experimented locally with permutation SHAP using both real background
rows and an all-missing reference, while keeping the fitted context and
ensemble fixed. These experiments are exploratory; I would prefer guidance
and validation from the model authors.

Requested functionality

Ideally, an official API or example would provide:

  • Local contributions for a selected class probability.
  • Explicit baseline and masking semantics.
  • Support for numerical/categorical features and missing values.
  • Contributions mapped back to the original input features.
  • Compatibility with fitted/cached contexts and clear ensemble semantics.
  • Configurable computational budgets and reproducibility.
  • Standard outputs usable with SHAP plots or Shapash local plots.

For additive methods, an example checking that baseline plus contributions
reconstructs the explained output would also be helpful.

An initial tutorial documenting the recommended approach and its
limitations would already be valuable.

Is this on the roadmap, or is there an existing workflow I have missed?

Lingua principale
Python
Stelle
429
Fork
46
Merge medio
1g 3h
PR unite (30g)
231

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.

Altre issue di NVIDIA/structured-data-models

Tutte le issue di NVIDIA/structured-data-models

Issue simili

Altre issue su Python

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.