# MingchaoZhu/InterpretableMLBook

《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版

Repository: https://github.com/MingchaoZhu/InterpretableMLBook
Canonical: https://ross.abutalabs.com/products/interpretablemlbook
License: GPL-3.0
License Family: copyleft
Last push: 2023-11-28T00:24:17+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2324, "days_push": 1010, "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 4897, forks 682 (observed 2026-08-28T04:09:02.061997+00:00)

## What it is
A Chinese translation of Christoph Molnar's 'Interpretable Machine Learning' book, hosted on GitHub with downloadable releases. It systematically covers interpretable models and model-agnostic explanation methods like feature importance, Shapley values, and LIME.

## Use cases
- learn interpretable machine learning in Chinese
- understand how to explain black-box model predictions
- study LIME and Shapley values with theory and examples
- find a systematic reference on model explainability methods
- make machine learning models transparent for regulated domains like finance or healthcare
- prepare to read new interpretability papers on arxiv

## When to choose
- you read Chinese and want a free, complete translation of the leading interpretable ML book
- you need both intuitive explanations and mathematical rigor for explainability methods
- you want critical discussion of each method's pros and cons tested on real data

## When to avoid
- you need the latest edition updates directly from the author
- you want hands-on Python code exercises, which are only planned as a future addition
- you prefer an English-language resource, in which case use the original book

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, nlp, documentation
- domain: machine-learning, tutorials, data-science, education
- platform: cross-platform
- tags: interpretable-ml, chinese-translation, book, explainability, shapley-values, lime, xai

## Member repositories
- MingchaoZhu/InterpretableMLBook (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:02.061997+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 2026-08-29T18:18:09.929771+00:00, confidence not recorded.
  - readme: https://github.com/MingchaoZhu/InterpretableMLBook (fetched 2026-08-28T04:09:02.061997+00:00, sha d8f710dde522)
- Data as of 2026-08-30T08:39:29.467469+00:00.