rb-libsvm provides Ruby bindings for the LIBSVM machine learning library.
Open source
24 alternativesML.NET
ML.NET provides an open source, cross platform machine learning framework for .NET applications.
About ML.NET
ML.NET is an open-source, cross-platform machine learning framework for .NET applications. It allows developers to build, train, deploy, and consume custom models, with data loading from files and databases, data transformations, and built-in algorithms for scenarios including classification, forecasting, and anomaly detection. It can also consume TensorFlow and ONNX models.
It runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework, with 64-bit supported on all platforms. Support is limited on ARM64, Apple M1, and Blazor Web Assembly, and 32-bit support is Windows-only without TensorFlow and LightGBM functionality.
Key features
- Build, train, deploy, and consume custom models in .NET applications
- Load data from files and databases
- Apply data transformations before training
- Train models for classification, forecasting, and anomaly detection
- Consume TensorFlow and ONNX models within ML.NET
- Install via NuGet package Microsoft.ML
Best for: .NET developers who want to add custom machine learning to applications without using Python or R.
Good for
- Open source and cross-platform for .NET applications
- Designed for use without prior machine learning expertise or Python/R experience
- Extensible through TensorFlow and ONNX model consumption
Watch out for
- Has some limitations on ARM64, Apple M1, and Blazor Web Assembly
- 32-bit support is Windows-only and excludes TensorFlow and LightGBM functionality
- Requires .NET Core 2.1 or later, or .NET Framework 4.6.1 or later
ML.NET at a glance
- Category
- Development › Programming Languages
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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ML.NET FAQ
What operating systems and architectures does ML.NET support?
It runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework, with 64-bit support on all platforms. It also runs on ARM64, Apple M1, and Blazor Web Assembly with some limitations.
How do I add ML.NET to an application?
After installing .NET Core 2.1 or later or .NET Framework 4.6.1 or later (4.7.2 or later recommended), install the Microsoft.ML NuGet package with `dotnet add package Microsoft.ML` or `Install-Package Microsoft.ML`.
What types of models can I train with ML.NET?
You can train models for scenarios including classification, forecasting, and anomaly detection.
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