Dod-o/Statistical-Learning-Method_Code resource

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11624 stars · 2882 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

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

learning-resource · maturity active

machine-learning developer-tools machine-learning tutorials education python statistical-learning li-hang from-scratch-implementations annotated-code chinese supervised-learning unsupervised-learning

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