Mlpack 3: a Fast, Flexible Machine Learning Library

In the past several years, the field of machine learning has seen an explosion of interest and excitement, with hundreds or thousands of algorithms developed for different tasks every year. But a primary problem faced by the field is the ability to scale to larger and larger data—since it is known that training on larger datasets typically produces better results (Halevy, Norvig, and Pereira 2009). Therefore, the development of new algorithms for the continued growth of the field depends largely on the existence of good tooling and libraries that enable researchers and practitioners to quickly prototype and develop solutions (Sonnenburg et al. 2007). Simultaneously, useful libraries must also be efficient and well-implemented. This has motivated our development of mlpack.