Papers › Designing and building the mlpack open-source machine learning library

Designing and building the mlpack open-source machine learning library

17 Aug 2017arXiv:1708.05279archive 2025-07-28

Ryan R. Curtin, Marcus Edel

mlpack is an open-source C++ machine learning library with an emphasis on speed and flexibility. Since its original inception in 2007, it has grown to be a large project implementing a wide variety of machine learning algorithms, from standard techniques such as decision trees and logistic regression to modern techniques such as deep neural networks as well as other recently-published cutting-edge techniques not found in any other library. mlpack is quite fast, with benchmarks showing mlpack outperforming other libraries' implementations of the same methods. mlpack has an active community, with contributors from around the world---including some from PUST. This short paper describes the goals and design of mlpack, discusses how the open-source community functions, and shows an example usage of mlpack for a simple data science problem.

PaperPDFCode

Code

mlpack/mlpack mentioned in paper report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

BIG-bench Machine Learning

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Logistic Regression

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections