[R-sig-eco] R-sig-ecology Digest, Vol 132, Issue 10

[Ralf Schäfer]

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 Schäfer

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

Prof. Dr. Ralf Bernhard Schäfer
Professor for Quantitative Landscape Ecology
Environmental Scientist (M.Sc.)
Institute for Environmental Sciences
University Koblenz-Landau
Fortstrasse 7
76829 Landau
Germany
Mail: schaefer-ralf using 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 using r-project.org:
> 
> 
> Message: 1
> Date: Thu, 21 Mar 2019 12:40:54 -0100
> From: Lara Silva <lara.sfp.silva using gmail.com <mailto:lara.sfp.silva using gmail.com>>
> To: r-sig-ecology using r-project.org <mailto:r-sig-ecology using r-project.org>
> Subject: [R-sig-eco] Calculate AIC, DIC and BIC for models machine
> 	learning
> Message-ID:
> 	<CALN9TETOhnxS6OuBZhMT6_YFRggND5Zf9zLkkk5icpN4UbSVTg using mail.gmail.com <mailto:CALN9TETOhnxS6OuBZhMT6_YFRggND5Zf9zLkkk5icpN4UbSVTg using mail.gmail.com>>
> Content-Type: text/plain; charset="utf-8"
> 
> 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


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