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Hi Steve<br>
<br>
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?<br>
<br>
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.<br>
<br>
Cheers,<br>
<br>
Paulo<br>
<br>
[1]Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R.K., &
Thuiller, W. 2009. Evaluation of consensus methods in predictive
species distribution modelling. <i>Diversity and Distributions</i>
15: 59–69.<br>
<br>
<div style="line-height: 1.35; padding-left: 2em; text-indent:-2em;"
class="csl-bib-body"> <span class="Z3988"
title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Evaluation%20of%20consensus%20methods%20in%20predictive%20species%20distribution%20modelling&rft.jtitle=Diversity%20and%20Distributions&rft.volume=15&rft.issue=1&rft.aufirst=M.&rft.aulast=Marmion&rft.au=M.%20Marmion&rft.au=M.%20Parviainen&rft.au=M.%20Luoto&rft.au=R.%20K%20Heikkinen&rft.au=W.%20Thuiller&rft.date=2009&rft.pages=59%E2%80%9369"></span></div>
<br>
<div class="moz-cite-prefix">On 27-03-16 00:47, Steven Pawley wrote:<br>
</div>
<blockquote
cite="mid:5B30AE9B1DA9F412.6E7BBE94-7E43-4D17-8270-60D7771CE92F@mail.outlook.com"
type="cite">
<div id="compose" style="padding-left: 20px; padding-right: 20px;
padding-bottom: 8px;" contenteditable="true">
<div>Hi Vaclaw and Paulo,</div>
<div><br>
</div>
<div>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.</div>
<div><br>
</div>
<div>Paulo thanks for running a few tests. It looks there is an
error with the class_weight parameter, I'll check into that.</div>
<div><br>
</div>
<div>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.</div>
<div><br>
</div>
<div>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.</div>
<div><br>
</div>
<div>Steve</div>
</div>
<div class="gmail_quote">_____________________________<br>
From: Vaclav Petras <<a moz-do-not-send="true" dir="ltr"
href="mailto:wenzeslaus@gmail.com"
x-apple-data-detectors="true"
x-apple-data-detectors-type="link"
x-apple-data-detectors-result="0">wenzeslaus@gmail.com</a>><br>
Sent: Saturday, March 26, 2016 11:21 AM<br>
Subject: Re: [GRASS-dev] RandomForest classifier for imagery
groups add-on<br>
To: Steven Pawley <<a moz-do-not-send="true" dir="ltr"
href="mailto:dr.stevenpawley@gmail.com"
x-apple-data-detectors="true"
x-apple-data-detectors-type="link"
x-apple-data-detectors-result="3">dr.stevenpawley@gmail.com</a>><br>
Cc: <<a moz-do-not-send="true" dir="ltr"
href="mailto:grass-dev@lists.osgeo.org"
x-apple-data-detectors="true"
x-apple-data-detectors-type="link"
x-apple-data-detectors-result="4">grass-dev@lists.osgeo.org</a>><br>
<br>
<br>
<div dir="ltr">
<div class="gmail_extra"> <br>
<div class="gmail_quote"> On Sat, Mar 26, 2016 at 12:40 PM,
Steven Pawley <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:dr.stevenpawley@gmail.com"><a class="moz-txt-link-abbreviated" href="mailto:dr.stevenpawley@gmail.com">dr.stevenpawley@gmail.com</a></a>></span>
wrote: <br>
<blockquote class="gmail_quote" style="margin:0px 0px 0px
0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex"> 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. </blockquote>
</div>
<br>
</div>
<div class="gmail_extra"> 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
<a moz-do-not-send="true" href="http://v.class.ml">v.class.ml</a>
and v.class.mlpy [3]. <br>
<br>
</div>
<div class="gmail_extra"> Vaclav <br>
</div>
<div class="gmail_extra"> <br>
[1] <a moz-do-not-send="true"
href="https://trac.osgeo.org/grass/wiki/Submitting/Docs#Images">https://trac.osgeo.org/grass/wiki/Submitting/Docs#Images</a>
<br>
[2] <a moz-do-not-send="true"
href="https://grass.osgeo.org/download/sample-data/">https://grass.osgeo.org/download/sample-data/</a>
<br>
[3] <a moz-do-not-send="true"
href="https://trac.osgeo.org/grass/changeset/66482/">https://trac.osgeo.org/grass/changeset/66482/</a>
<br>
</div>
</div>
<br>
<br>
</div>
</blockquote>
<br>
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