[GRASS-dev] RandomForest classifier for imagery groups add-on

Paulo van Breugel p.vanbreugel at gmail.com
Sun Mar 27 02:11:08 PDT 2016

Hi Steve

Yes, your user case will not differ methodologically from species 
modeling based on presence/absence. One reason I was asking for the 
regression randomForest is that in one article (can't remember the 
title, will look it up) it was found that the regression approach 
yielded better results, even though the response variable is binary. One 
your help page, you write that r.randomforest performs random forest 
classification and regression, and the regression mode can be used by 
setting the mode to the regression option. But I am not seeing that option?

Great you are planning other methods as well. Giving model uncertainties 
(quite an issue in species distribution modeling), having multiple 
methods is really a plus, especially as it allows one to build consensus 
models [1] and combine them to create uncertainty maps.



[1]Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R.K., & Thuiller, 
W. 2009. Evaluation of consensus methods in predictive species 
distribution modelling. /Diversity and Distributions/ 15: 59–69.

On 27-03-16 00:47, Steven Pawley wrote:
> Hi Vaclaw and Paulo,
> Thanks for those pointers re. lazy technique and documentation. I have 
> a RandomForest diagram to explain the process, as well as some 
> examples, so I'll update documentation next week.
> Paulo thanks for running a few tests. It looks there is an error with 
> the class_weight parameter, I'll check into that.
> In terms of species distribution modelling, I have been using the tool 
> for landslide susceptibility modelling, which I believe is 
> methodologically similar to SDM in terms of having a binary response 
> variable. I have been doing this for the area of Alberta, using an 
> 8000 x 14000 pixel and 17 band stack of predictors. In the case of a 
> binary response variable, the usual approach is to run random forest 
> in classification mode, i.e. with fully grown trees, but use the class 
> probabilities to represent the 'species' or 'landslide' index.
> I am planning to implement other methods in the scikit learn package, 
> which represents a trivial change to the module once he bugs are 
> ironed out. I will probably look to create modules for SVM and 
> logistic regression, and maybe  nearest neighbours classification. 
> Certainly open to any suggestions.
> Steve
> _____________________________
> From: Vaclav Petras <wenzeslaus at gmail.com <mailto:wenzeslaus at gmail.com>>
> Sent: Saturday, March 26, 2016 11:21 AM
> Subject: Re: [GRASS-dev] RandomForest classifier for imagery groups add-on
> To: Steven Pawley <dr.stevenpawley at gmail.com 
> <mailto:dr.stevenpawley at gmail.com>>
> Cc: <grass-dev at lists.osgeo.org <mailto:grass-dev at lists.osgeo.org>>
> On Sat, Mar 26, 2016 at 12:40 PM, Steven Pawley 
> <dr.stevenpawley at gmail.com <mailto:dr.stevenpawley at gmail.com>> wrote:
>     I would like to draw your attention to a new GRASS add-on,
>     r.randomforest, which uses the scikit-learn and pandas Python
>     packages to classify GRASS rasters. 
> Thanks, this looks good. Please consider adding an image to the 
> documentation to better promote the module [1] and also an example 
> which would work with the NC SPM dataset [2]. For the addon to 
> generate documentation on the server and work well at few other 
> special occasions, it is advantageous to employ lazy import technique 
> for the non-standard dependencies, see for example v.class.ml 
> <http://v.class.ml> and v.class.mlpy [3].
> Vaclav
> [1] https://trac.osgeo.org/grass/wiki/Submitting/Docs#Images
> [2] https://grass.osgeo.org/download/sample-data/
> [3] https://trac.osgeo.org/grass/changeset/66482/

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