# ytzfhqs/AAAMLP-CN

Approaching (Almost) Any Machine Learning Problem中译版，在线文档地址：https://ytzfhqs.github.io/AAAMLP-CN/

Repository: https://github.com/ytzfhqs/AAAMLP-CN
Canonical: https://ross.abutalabs.com/products/aaamlp-cn
Language: Jupyter Notebook
License Family: other
Last push: 2025-12-10T11:47:46+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 56, release rhythm 8, longevity 78
- inputs: {"age_days": 1099, "days_push": 266, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1982, forks 262 (observed 2026-08-28T04:06:01.851254+00:00)

## What it is
A Chinese translation of Abhishek Thakur's book 'Approaching (Almost) Any Machine Learning Problem', hosted as an online documentation site with downloadable Markdown and EPUB versions. It covers practical ML topics like cross-validation, feature engineering, hyperparameter optimization, and ensembling.

## Use cases
- learn machine learning in chinese
- read aaamlp book online
- study cross-validation and evaluation metrics
- learn feature engineering and feature selection
- prepare for kaggle competitions
- find a free machine learning book translation
- learn hyperparameter tuning and model ensembling

## When to choose
- you prefer reading ML material in Chinese
- you want a free, practical, example-driven ML book
- you are preparing for Kaggle or applied ML work

## When to avoid
- you need the original English text
- you need a license-clear redistribution of the content
- you want a rigorous theoretical treatment of ML

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science, documentation
- domain: machine-learning, data-science, tutorials, education
- platform: python
- tags: book-translation, chinese-translation, kaggle, abhishek-thakur, jupyter-notebook, online-reading, epub, web-server

## Member repositories
- ytzfhqs/AAAMLP-CN (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:01.851254+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 2026-08-30T03:03:56.775128+00:00, confidence not recorded.
  - readme: https://github.com/ytzfhqs/AAAMLP-CN (fetched 2026-08-28T04:06:01.851254+00:00, sha a7adcbee78cd)
- Data as of 2026-08-30T08:39:29.467469+00:00.