# dotnet/machinelearning-samples

Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.

Repository: https://github.com/dotnet/machinelearning-samples
Canonical: https://ross.abutalabs.com/products/machinelearning-samples
Homepage: https://dot.net/ml
Language: PowerShell
License: MIT
License Family: permissive
Topics: machine-learning, algorithms, dotnet, csharp, ml
Last push: 2024-07-27T09:39:50+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3022, "days_push": 767, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4686, forks 2672 (observed 2026-08-28T04:08:56.977052+00:00)

## What it is
The official samples repository for ML.NET, Microsoft's cross-platform machine learning framework for .NET. It contains getting-started console samples for each ML task (in C# and F#) as well as end-to-end web and desktop apps infused with ML.NET models.

## Use cases
- learn how to do sentiment analysis in C# with ML.NET
- example code for training a machine learning model in .NET
- spam detection sample for .NET developers
- credit card fraud detection example with ML.NET
- build a recommendation system in C#
- end-to-end sample app with machine learning in .NET
- get started with ML.NET as a beginner

## When to choose
- you are a .NET developer learning machine learning with ML.NET
- you want copy-paste-ready C# or F# examples for common ML tasks
- you need reference implementations of ML scenarios like classification, regression, or recommendation

## When to avoid
- you need a production machine learning framework itself rather than examples
- you work outside the .NET ecosystem (e.g., Python, JVM)
- you need cutting-edge deep learning research code

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, nlp, data-science
- domain: machine-learning, tutorials, developer-tools
- platform: cross-platform, windows, dotnet
- tags: ml.net, samples, csharp, fsharp, binary-classification, sentiment-analysis, recommendation, anomaly-detection, automl, linux, macos

## Member repositories
- dotnet/machinelearning-samples (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:56.977052+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 2026-08-29T18:19:17.172635+00:00, confidence not recorded.
  - readme: https://github.com/dotnet/machinelearning-samples (fetched 2026-08-28T04:08:56.977052+00:00, sha 03ea175b6733)
  - homepage: https://dot.net/ml (fetched 2026-08-29T09:03:21.228499+00:00, sha 4e0f91861335)
  - site_page: https://learn.microsoft.com/docs (fetched 2026-08-29T09:03:21.240164+00:00, sha f29800be2b9a)
  - site_page: https://learn.microsoft.com/en-us/dotnet (fetched 2026-08-29T09:03:21.238005+00:00, sha efa9016b58aa)
- Data as of 2026-08-30T08:39:29.467469+00:00.