R-sig-ecology Digest, Vol 132, Issue 10

Hi Lara,

I am actually not sure that this is the best way to proceed.
Cross-validation seems the method of choice and depending on your purpose you can compare the prediction error between models.
See: Hauenstein S., Wood S.N. & Dormann C.F. (2018). Computing AIC for black-box models using generalized degrees of freedom: A comparison with cross-validation. Communications in Statistics - Simulation and Computation 47, 1382?1396. https://doi.org/10.1080/03610918.2017.1315728 <https://doi.org/10.1080/03610918.2017.1315728>

However, these authors provide code to derive an AIC for different machine learning approaches. https://github.com/biometry/GDF

Hope this helps and have a nice weekend.

Best regards,

Ralf Sch?fer

------------------------------------------------------------

Prof. Dr. Ralf Bernhard Sch?fer
Professor for Quantitative Landscape Ecology
Environmental Scientist (M.Sc.)
Institute for Environmental Sciences
University Koblenz-Landau
Fortstrasse 7
76829 Landau
Germany
Mail: schaefer-ralf at uni-landau.de
Phone: ++49 (0) 6341 280-31536
Web: www.landscapecology.uni-landau.de

Am 22.03.2019 um 12:00 schrieb r-sig-ecology-request at r-project.org:


Message: 1
Date: Thu, 21 Mar 2019 12:40:54 -0100
From: Lara Silva <lara.sfp.silva at gmail.com <mailto:lara.sfp.silva at gmail.com>>
To: r-sig-ecology at r-project.org <mailto:r-sig-ecology at r-project.org>
Subject: [R-sig-eco] Calculate AIC, DIC and BIC for models machine
	learning
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Hello everyone!

In R, it is possible to calculate AIC, DIC, or BIC  for models machine
learning, like RF, ANN, GBM, MARS?

Are there any functions or specific packages in R?

Any suggestion?

Thanks

Lara