A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction

ABSTRACT: The adoption of machine learning frameworks in areas beyond computer science have been facilitated by the development of user-friendly software tools that do not require an advanced understanding of computer programming. In this paper, we present a new package (sparse kernel methods, SKM) software developed in R language for implementing six (generalized boosted machines, generalized linear models, support vector machines, random forest, Bayesian regression models and deep neural networks) of the most popular supervised machine learning algorithms with the optional use of sparse kernels. The SKM focuses on user simplicity, as it does not try to include all the available machine learning algorithms, but rather the most important aspects of these six algorithms in an easy-to-understand forma

SUBMITTER: Montesinos Lopez O 

PROVIDER: S-EPMC9205295 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature