mlpack

Initial release	February 1, 2008; 15 years ago (2008-02-01)[1]
Stable release	4.3.0[2] / 27 November 2023; 2 months ago (27 November 2023)
Repository	github.com/mlpack/mlpack
Written in	C++, Python, Julia, Go
Operating system	Cross-platform
Available in	English
Type	Software library Machine learning
License	Open source (BSD)
Website	mlpack.org

mlpack is a machine learning software library for C++, built on top of the Armadillo library and the ensmallen numerical optimization library.^[3] mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users.^[4] Its intended target users are scientists and engineers.

It is open-source software distributed under the BSD license, making it useful for developing both open source and proprietary software. Releases 1.0.11 and before were released under the LGPL license. The project is supported by the Georgia Institute of Technology and contributions from around the world.

Miscellaneous features

Class templates for GRU, LSTM structures are available, thus the library also supports Recurrent Neural Networks.

There are bindings to R, Go, Julia,^[5] and Python. Its binding system is extensible to other languages.

Supported algorithms

Currently mlpack supports the following algorithms and models:

• Collaborative Filtering
• Decision stumps (one-level decision trees)
• Density Estimation Trees
• Euclidean minimum spanning trees
• Gaussian Mixture Models (GMMs)
• Hidden Markov Models (HMMs)
• Kernel density estimation (KDE)
• Kernel Principal Component Analysis (KPCA)
• K-Means Clustering
• Least-Angle Regression (LARS/LASSO)
• Linear Regression
• Bayesian Linear Regression
• Local Coordinate Coding
• Locality-Sensitive Hashing (LSH)
• Logistic regression
• Max-Kernel Search
• Naive Bayes Classifier
• Nearest neighbor search with dual-tree algorithms
• Neighbourhood Components Analysis (NCA)
• Non-negative Matrix Factorization (NMF)
• Principal Components Analysis (PCA)
• Independent component analysis (ICA)
• Rank-Approximate Nearest Neighbor (RANN)
• Simple Least-Squares Linear Regression (and Ridge Regression)
• Sparse Coding, Sparse dictionary learning
• Tree-based Neighbor Search (all-k-nearest-neighbors, all-k-furthest-neighbors), using either kd-trees or cover trees
• Tree-based Range Search