# fengdu78/lihang-code

《统计学习方法》的代码实现

Repository: https://github.com/fengdu78/lihang-code
Canonical: https://ross.abutalabs.com/products/lihang-code
Language: Jupyter Notebook
License Family: other
Last push: 2023-08-22T09:26:05+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2837, "days_push": 1107, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 19590, forks 6238 (observed 2026-08-28T04:11:28.937727+00:00)

## What it is
A collection of Jupyter Notebook code implementations accompanying Li Hang's textbook 'Statistical Learning Methods' (2nd edition). It covers classic supervised and unsupervised algorithms such as perceptron, kNN, naive Bayes, decision trees, logistic regression, SVM, boosting, EM, HMM, CRF, clustering, SVD, PCA, LDA, MCMC, and PageRank.

## Use cases
- learn machine learning algorithms by reading runnable code
- study the algorithms from the Statistical Learning Methods textbook
- implement classic ML algorithms from scratch in Python
- prepare for machine learning interviews
- find reference implementations of SVM, EM, HMM, or LDA

## When to choose
- you are studying Li Hang's Statistical Learning Methods book and want code for each chapter
- you want simple, educational from-scratch implementations of classic ML algorithms
- you prefer learning through Jupyter notebooks

## When to avoid
- you need production-ready, optimized, or well-tested ML code
- you want a maintained library with an API rather than educational notebooks
- you need deep learning or modern large-scale methods

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, education, tutorials
- platform: python
- tags: jupyter-notebooks, statistical-learning, textbook-companion, chinese, supervised-learning, unsupervised-learning

## Member repositories
- fengdu78/lihang-code (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:28.937727+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 2026-08-29T16:59:54.563883+00:00, confidence not recorded.
  - readme: https://github.com/fengdu78/lihang-code (fetched 2026-08-28T04:11:28.937727+00:00, sha 987e339c9d8b)
- Data as of 2026-08-30T08:39:29.467469+00:00.