mlpack

What it does

mlpack is a header-only C++ machine-learning library for developers and data-science practitioners building or prototyping applications. It provides algorithms through a C++ API, language bindings for Python, Julia, R and Go, and command-line programs. Users supply data to the library or its command-line tools and use the selected algorithms in their own programs or workflows; documented areas include preprocessing, classification, regression, clustering and model evaluation.

Documentation covers numeric, mixed categorical, image and audio data, plus dataset splitting, feature extraction, cross-validation and hyperparameter tuning. Install the library from a source release or a supported package manager, then use the interface matching your application. JupyterHub and Docker options are also available for exploration. mlpack is distributed under the permissive three-clause BSD license; it provides software rather than managed inference or a complete hosted application. Package versions can lag behind source releases, and applications remain responsible for their own data preparation, evaluation and computing resources.

What it can do

• Machine-learning algorithms for classification, regression and clustering
• Data loading for numeric, mixed categorical, image and audio data
• Preprocessing and feature extraction
• Cross-validation and hyperparameter tuning
• C++ API, Python, Julia, R and Go bindings
• Command-line programs

Works with

• Armadillo
• ensmallen
• cereal
• JupyterHub
• Docker

Before you choose

• Prebuilt packages may not contain the latest release; the download page recommends the official source release when encountering issues.
• mlpack is a library and set of command-line programs, not a hosted general-purpose web application.
• Hosted notebooks, containers and your own applications may require separately funded compute.

Listed under

• Data
• Algorithms
• C++
• Developer tools
• Library
• Machine learning
• Open source

Get started

1. Choose a supported installation route on the official download page, such as a system package, language package or source release.
2. Install mlpack and its prerequisites for the selected interface; use the platform-specific build guide if needed.
3. Follow the matching C++, Python, Julia, R, Go or command-line quickstart.

No software subscription; deployment and computing resources are separate.

mlpack is free to use. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 1 Nov 2026. Each source supports part of the overview; availability and prices can change.

• mlpack - Home ↗www.mlpack.org
• mlpack documentation ↗www.mlpack.org
• mlpack - Download ↗www.mlpack.org
• mlpack - Home ↗www.mlpack.org

Company website

mlpack.org: product, pricing and policy pages

YC directory mirror

Batch and company name

Human review

Checked by a person

knowledge_base

manual_review

Last checked 1 Nov 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.