# Jack-Cherish/Machine-Learning

:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

Repository: https://github.com/Jack-Cherish/Machine-Learning
Canonical: https://ross.abutalabs.com/products/machine-learning
Homepage: https://cuijiahua.com/blog/ml/
Language: Python
License Family: other
Topics: adaboost, logistic, svm, smo, knn, machine-learning, python, python3, navie-bayes-algorithm, decision-tree, adaboost-algorithm, regression
Last push: 2024-07-12T11:28:35+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": 3448, "days_push": 782, "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 10378, forks 5065 (observed 2026-08-28T04:10:41.834714+00:00)

## What it is
A Python3 code companion and tutorial series for the classic 'Machine Learning in Action' book, implementing kNN, decision trees, naive Bayes, logistic regression, SVM, linear/tree regression, and AdaBoost from scratch. It is paired with extensive Chinese-language blog articles by Jack Cui explaining the theory and practice of each algorithm.

## Use cases
- learn machine learning algorithms from scratch in python
- implement knn and decision trees without sklearn
- understand svm and smo algorithm derivation with code
- study naive bayes text classification example
- practice logistic regression with gradient ascent
- follow a machine learning in action study guide
- learn cart regression trees and pruning

## When to choose
- you want to learn classic ML algorithms by hand-coding them in Python3
- you prefer tutorial-style explanations paired with runnable example code
- you are studying the 'Machine Learning in Action' book and want updated Python3 code

## When to avoid
- you need a production ML library - use scikit-learn instead
- you need deep learning, neural networks, or GPU acceleration
- you require a maintained, licensed package with pip installation and tests

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, developer-tools
- domain: machine-learning, tutorials, education
- platform: python, cross-platform
- tags: knn, decision-tree, naive-bayes, logistic-regression, svm, adaboost, regression, python3, machine-learning-in-action, chinese, algorithms

## Member repositories
- Jack-Cherish/Machine-Learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:41.834714+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 2026-08-29T17:19:05.792019+00:00, confidence not recorded.
  - readme: https://github.com/Jack-Cherish/Machine-Learning (fetched 2026-08-28T04:10:41.834714+00:00, sha 2e9c8894bc39)
  - homepage: https://cuijiahua.com/blog/ml/ (fetched 2026-08-29T08:18:26.859368+00:00, sha 9f61b13cf276)
  - site_page: https://cuijiahua.com/netscope/quickstart.html (fetched 2026-08-29T08:18:26.868356+00:00, sha 55e1ba0d2a33)
  - site_page: https://cuijiahua.com/about.html (fetched 2026-08-29T08:18:26.869996+00:00, sha 6cbd435b36e1)
- Data as of 2026-08-30T08:39:29.467469+00:00.