The Accord.MachineLearning package includes .NET algorithms for support vector machines, decision trees, and clustering.

nuget.org/packages/Accord.MachineLearning
Accord.MachineLearning homepage screenshot

About Accord.MachineLearning

Accord.MachineLearning 3.8.0 is a .NET package that is part of the Accord.NET Framework. It contains Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models, and general algorithms such as Ransac, Cross-validation and Grid-Search.

The listed version targets .NET Standard 1.4 and .NET Framework 3.5 or higher. A newer prerelease version is also listed.

Key features

  • Support Vector Machines for machine-learning applications
  • Decision Trees for machine-learning applications
  • Naive Bayesian models
  • K-means clustering
  • Gaussian Mixture models
  • Ransac, Cross-validation and Grid-Search algorithms
Best for: Developers building .NET applications that use classification, clustering, or model validation algorithms.
Good for
  • Part of the Accord.NET Framework
  • Targets .NET Standard 1.4 and .NET Framework 3.5 or higher
  • Combines classifiers, clustering models and validation utilities in one package

Accord.MachineLearning at a glance

Type
Website
Pricing
Not confirmed. Check the site
Platforms
Web
Open source
Not stated
Language
English
Last checked
Oct 5, 2026 · Online when checked
Listed
Oct 5, 2026

Accord.MachineLearning alternatives

Tools and sites you could use instead, ranked by how closely they match.

All 24 alternatives

Popular in ML Training & Optimization

Well-known listings from the same topic.

All 75

Accord.MachineLearning FAQ

What frameworks does Accord.MachineLearning 3.8.0 target?
.NET Standard 1.4 and .NET Framework 3.5 or higher.
How is the package installed with the .NET CLI?
Run dotnet add package Accord.MachineLearning --version 3.8.0.
Is something wrong or out of date?

Tell us and an editor will check it.

Own this website?

Claim the listing and suggest updates yourself. An editor checks every claim first.

Claim this listing