# TeFuirnever/Machine-Learning-in-Action

⚡️⚡️⚡️《机器学习实战》代码（基于Python3）🚀

Repository: https://github.com/TeFuirnever/Machine-Learning-in-Action
Canonical: https://ross.abutalabs.com/products/machine-learning-in-action
Homepage: https://blog.csdn.net/tefuirnever
Language: Python
License Family: other
Topics: knn, decisiontree, naivebayes, logistic, svm, adaboost, regression, regression-trees
Last push: 2020-02-05T11:37:01+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": 2521, "days_push": 2401, "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 1017, forks 279 (observed 2026-08-28T04:03:14.598202+00:00)

## What it is
Python 3 companion code for the book 'Machine Learning in Action', implementing classic ML algorithms chapter by chapter (KNN, decision trees, naive Bayes, logistic regression, SVM, AdaBoost, regression). It is paired with the author's CSDN blog tutorials for guided learning.

## Use cases
- learn machine learning algorithms from scratch in Python
- study KNN, decision trees, and naive Bayes implementations
- understand how SVM and AdaBoost work with example code
- practice regression and regression trees on real datasets
- follow a book-based machine learning tutorial with code
- get example datasets for classic ML algorithms

## When to choose
- you are learning ML fundamentals and want readable, from-scratch Python implementations
- you are reading 'Machine Learning in Action' and want working code per chapter
- you prefer algorithm code without heavy framework dependencies

## When to avoid
- you need production-ready or well-tested ML libraries
- you want deep learning or modern frameworks like PyTorch/TensorFlow
- you need a maintained, licensed package with active development

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

## Member repositories
- TeFuirnever/Machine-Learning-in-Action (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:14.598202+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 2026-08-30T07:11:09.953229+00:00, confidence not recorded.
  - readme: https://github.com/TeFuirnever/Machine-Learning-in-Action (fetched 2026-08-28T04:03:14.598202+00:00, sha 217db21ac177)
  - homepage: https://blog.csdn.net/tefuirnever (fetched 2026-08-29T13:10:02.775777+00:00, sha 7c9a79aa9975)
- Data as of 2026-08-30T08:39:29.467469+00:00.