SleepyBag/Statistical-Learning-Methods resource

Health v2 · maintenance only

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Adoption not part of the score

1175 stars · 276 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python implementation of every algorithm from Li Hang's textbook 'Statistical Learning Methods', built from scratch with NumPy matrix operations. It covers models like perceptron, kNN with kd-tree, naive Bayes, decision trees, logistic regression, max entropy, HMM with Baum-Welch, and linear chain CRFs, each with runnable examples.

Use cases

• study algorithms from the Statistical Learning Methods book with working code
• see a from-scratch implementation of the max entropy model
• learn how a linear chain conditional random field is implemented
• understand HMM training with the Baum-Welch algorithm
• compare ID3, C4.5, and CART decision tree implementations
• find vectorized NumPy versions of classic ML algorithms

When to choose

• you are reading Li Hang's book and want code for every chapter
• you want educational, detail-complete implementations rather than production libraries
• you need rare implementations like max entropy or linear chain CRF from scratch

When to avoid

• you need a production-ready, well-tested ML library
• you require a maintained package with a license and installable releases
• you want GPU-accelerated or deep learning frameworks

Facets

learning-resource · maturity maintenance

machine-learning data-science machine-learning education tutorials python cross-platform statistical-learning li-hang textbook-implementations educational numpy algorithms-from-scratch

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