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

Steven Pawley dr.stevenpawley at gmail.com
Sat Mar 26 16:47:22 PDT 2016

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.
From: Vaclav Petras <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>
Cc:  <grass-dev at lists.osgeo.org>

         On Sat, Mar 26, 2016 at 12:40 PM, Steven Pawley      <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 and v.class.mlpy [3].    
[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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