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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
Browse package contents
Package details |
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| Author | Kyle Peterson [aut, cre] |
| Maintainer | Kyle Peterson <petersonkdon@gmail.com> |
| License | GPL-2 |
| Version | 1.2.1 |
| URL | http://mlf-project.us/ |
| Package repository | View on CRAN |
| Installation |
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