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 gmail.com> |
License: |
GPL-2 |
URL: |
http://mlf-project.us/ |
NeedsCompilation: |
no |
CRAN checks: |
mlf results |
Documentation:
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