mlf: Machine Learning Foundations

Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.

Getting started

Package details

AuthorKyle Peterson [aut, cre]
MaintainerKyle Peterson <>
Package repositoryView on CRAN
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mlf documentation built on May 1, 2019, 10:34 p.m.