# wzyonggege/statistical-learning-method

《统计学习方法》笔记-基于Python算法实现

Repository: https://github.com/wzyonggege/statistical-learning-method
Canonical: https://ross.abutalabs.com/products/statistical-learning-method
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: python, statistical-learning
Last push: 2026-08-12T06:10:49+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 100
- inputs: {"age_days": 3170, "days_push": 21, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2180, forks 662 (observed 2026-08-28T04:06:23.210869+00:00)

## What it is
A collection of Jupyter notebooks implementing the algorithms from the classic Chinese textbook 'Statistical Learning Methods' (统计学习方法) in Python, covering least squares, perceptron, kNN, Naive Bayes, decision trees, logistic regression, SVM, AdaBoost, and EM. It is educational material first, with hand-written implementations and scikit-learn used only for comparison.

## Use cases
- learn statistical learning algorithms from scratch in python
- study the statistical learning methods textbook with code
- implement perceptron knn svm adaboost by hand
- notebooks explaining the math behind classic ml algorithms
- practice machine learning fundamentals with jupyter
- compare hand-written implementations with scikit-learn

## When to choose
- you are studying Li Hang's Statistical Learning Methods and want runnable code per chapter
- you want to understand classic ML algorithms by reading from-scratch implementations
- you prefer notebook-based, math-plus-code learning material

## When to avoid
- you need a production machine learning library
- you want modern deep learning or transformer-based methods
- you need actively developed features rather than maintained educational notes

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, developer-tools
- domain: machine-learning, education, tutorials, data-science
- platform: python, cross-platform
- tags: jupyter-notebooks, statistical-learning, chinese, textbook-companion, algorithms-from-scratch, scikit-learn

## Member repositories
- wzyonggege/statistical-learning-method (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:23.210869+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 2026-08-30T02:48:44.851870+00:00, confidence not recorded.
  - readme: https://github.com/wzyonggege/statistical-learning-method (fetched 2026-08-28T04:06:23.210869+00:00, sha df7d91578e88)
- Data as of 2026-08-30T08:39:29.467469+00:00.