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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 gmail.com> |
| License: | GPL-2 |
| URL: | http://mlf-project.us/ |
| NeedsCompilation: | no |
| CRAN checks: | mlf results |
| Reference manual: | mlf.pdf |
| Package source: | mlf_1.2.1.tar.gz |
| Windows binaries: | r-devel: mlf_1.2.1.zip, r-release: mlf_1.2.1.zip, r-oldrel: mlf_1.2.1.zip |
| 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, r-oldrel (x86_64): mlf_1.2.1.tgz |
| Old sources: | mlf archive |
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