# rasbt/pattern_classification

A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks

Repository: https://github.com/rasbt/pattern_classification
Canonical: https://ross.abutalabs.com/products/pattern_classification
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Topics: machine-learning, pattern-classification, machine-learning-algorithms
Last push: 2023-11-26T15:54:59+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": 4539, "days_push": 1011, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4211, forks 1276 (observed 2026-08-28T04:08:39.125322+00:00)

## What it is
A curated collection of tutorials, Jupyter notebooks, and examples covering pattern classification, machine learning, and data mining. It explains concepts like preprocessing, model evaluation, and classic algorithms (Bayes, logistic regression, neural networks, ensembles, clustering) with runnable Python code.

## Use cases
- learn machine learning fundamentals with worked examples
- understand pattern classification algorithms step by step
- see how to preprocess and scale features for classification
- study model evaluation and parameter estimation techniques
- find example notebooks for PCA and dimensionality reduction
- get references and resources for data mining topics

## When to choose
- you want tutorial-style notebooks explaining ML concepts with code
- you are learning scikit-learn-based classification workflows
- you need reference material on classic algorithms like Bayes classifiers and ensemble methods

## When to avoid
- you need a production-ready ML library or framework
- you want modern deep learning tutorials (content is mostly classical ML)
- you need actively updated content for the latest library versions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, data-visualization, nlp
- domain: machine-learning, data-science, tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebooks, pattern-classification, tutorials, scikit-learn, data-mining

## Member repositories
- rasbt/pattern_classification (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:39.125322+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 2026-08-29T18:22:29.581670+00:00, confidence not recorded.
  - readme: https://github.com/rasbt/pattern_classification (fetched 2026-08-28T04:08:39.125322+00:00, sha 1a1f3839ae41)
- Data as of 2026-08-30T08:39:29.467469+00:00.