# ModelOriented/DALEX

moDel Agnostic Language for Exploration and eXplanation

Repository: https://github.com/ModelOriented/DALEX
Canonical: https://ross.abutalabs.com/products/dalex
Homepage: https://dalex.drwhy.ai
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: machine-learning, interpretability, data-science, xai, iml, model-visualization, dalex, explanations, explainable-ai, explainable-artificial-intelligence, predictive-modeling, black-box, explainable-ml, interpretable-machine-learning, explanatory-model-analysis, responsible-ai, responsible-ml, fairness
Last push: 2026-07-15T11:00:33+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 92, release rhythm 8, longevity 100
- inputs: {"age_days": 3118, "days_push": 49, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1485, forks 171 (observed 2026-08-28T04:04:51.791764+00:00)

## What it is
DALEX (moDel Agnostic Language for Exploration and eXplanation) is a library for exploring, explaining, and visualizing the behavior of predictive machine learning models. It wraps any black-box model and provides local and global explainers (variable importance, partial dependence, Shapley values, LIME, Break Down, ceteris paribus profiles), plus fairness auditing and interactive dashboards.

## Use cases
- explain predictions from a black-box model like xgboost or keras
- compute feature importance and partial dependence profiles for a model
- compare explanations across models built in different frameworks
- audit a model for fairness and bias
- visualize how a model responds to changes in a single observation
- teach interpretable machine learning concepts
- explore model residuals and performance diagnostics

## When to choose
- you need model-agnostic explanations for any predictive model regardless of framework
- you want a comprehensive XAI toolkit with both local and global explainers in R or Python
- you need fairness/bias detection modules alongside explanations
- you want interactive dashboards (Arena) for exploring models

## When to avoid
- you need explanations specific to deep learning with gradient-based methods like integrated gradients
- you want a lightweight single-purpose explanation library rather than a full framework
- you need production-scale low-latency explanation serving

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-visualization, nlp
- domain: machine-learning, data-science
- platform: python
- tags: xai, explainable-ml, model-explanations, fairness, responsible-ai, black-box-models, predictive-modeling, explainable-ai, interpretability, r

## Member repositories
- ModelOriented/DALEX (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:51.791764+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:33:54.493719+00:00, confidence not recorded.
  - readme: https://github.com/ModelOriented/DALEX (fetched 2026-08-28T04:04:51.791764+00:00, sha 0e36782312df)
  - homepage: https://dalex.drwhy.ai (fetched 2026-08-29T11:40:12.655261+00:00, sha 6b78241674bb)
  - site_page: https://arena.drwhy.ai/docs/guide/basic-concepts (fetched 2026-08-29T11:40:12.672391+00:00, sha 5c0802b14b20)
  - registry_pypi: https://pypi.org/pypi/dalex/json (fetched 2026-08-29T11:40:12.674152+00:00, sha 0ffc94942bf7)
  - site_page: https://dalex.drwhy.ai/python-dalex-fifa.html (fetched 2026-08-29T11:40:12.670634+00:00, sha 812888997a49)
  - site_page: https://dalex.drwhy.ai/python/api (fetched 2026-08-29T11:40:12.664690+00:00, sha 5d87cb428317)
- Data as of 2026-08-30T08:39:29.467469+00:00.