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>.

Version: 1.2.1
Imports: stats, utils
Published: 2018-06-25
Author: Kyle Peterson [aut, cre]
Maintainer: Kyle Peterson <petersonkdon at>
License: GPL-2
NeedsCompilation: no
CRAN checks: mlf results


Reference manual: mlf.pdf


Package source: mlf_1.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlf_1.2.1.tgz, r-oldrel (arm64): mlf_1.2.1.tgz, r-release (x86_64): mlf_1.2.1.tgz
Old sources: mlf archive


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