# Dod-o/Statistical-Learning-Method_Code

手写实现李航《统计学习方法》书中全部算法

Repository: https://github.com/Dod-o/Statistical-Learning-Method_Code
Canonical: https://ross.abutalabs.com/products/statistical-learning-method_code
Language: Python
License Family: other
Topics: machine-learning-algorithms, code, statistical-learning-method
Last push: 2025-11-25T11:02:14+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 54, release rhythm 35, longevity 100
- inputs: {"age_days": 2848, "days_push": 281, "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 11624, forks 2882 (observed 2026-08-28T04:10:49.127738+00:00)

## What it is
A collection of hand-written, heavily commented Python implementations of all algorithms from Li Hang's textbook 'Statistical Learning Method' (统计学习方法), including perceptron, KNN, naive Bayes, decision trees, logistic regression, maximum entropy, and unsupervised algorithms. Each implementation cites the corresponding formulas, and companion blog posts explain the underlying principles.

## Use cases
- learn machine learning algorithms by reading from-scratch code
- understand how perceptron and KNN work internally
- study naive Bayes and decision tree implementations with formula references
- prepare for machine learning course exams or interviews
- reimplement classic statistical learning algorithms in Python
- follow along with Li Hang's textbook chapter by chapter

## When to choose
- you are studying Li Hang's Statistical Learning Method and want code matched to the book's formulas
- you learn best from small, fully commented from-scratch implementations
- you want Chinese-language explanations with accompanying blog posts

## When to avoid
- you need production-ready, optimized ML libraries for real workloads
- you want GPU acceleration or deep learning frameworks
- you need a maintained package with a license, tests, or an installable API

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, developer-tools
- domain: machine-learning, tutorials, education
- platform: python
- tags: statistical-learning, li-hang, from-scratch-implementations, annotated-code, chinese, supervised-learning, unsupervised-learning

## Member repositories
- Dod-o/Statistical-Learning-Method_Code (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:49.127738+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 2026-08-29T17:15:27.369217+00:00, confidence not recorded.
  - readme: https://github.com/Dod-o/Statistical-Learning-Method_Code (fetched 2026-08-28T04:10:49.127738+00:00, sha 633201bb759f)
- Data as of 2026-08-30T08:39:29.467469+00:00.