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.
Cheers,
Paulo
[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 <[email protected] <mailto:[email protected]>>
Sent: Saturday, March 26, 2016 11:21 AM
Subject: Re: [GRASS-dev] RandomForest classifier for imagery groups add-on
To: Steven Pawley <[email protected]
<mailto:[email protected]>>
Cc: <[email protected] <mailto:[email protected]>>
On Sat, Mar 26, 2016 at 12:40 PM, Steven Pawley
<[email protected] <mailto:[email protected]>> 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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