On 27-03-16 16:58, Steven Pawley wrote:
Hello Paulo,
Many thanks for this. I updated the mode last night to include the
ability to force regression mode, as well as including some more error
checking for valid combinations of input parameters. Classification
mode also checks that the input labelled pixels are CELL type. I'm not
outputting all of the appropriate uncertainty measures like RSQ yet
for regression mode yet, but I'll add those in.
Great, I'll check it out.
That is interesting that you had better performance when using
regression. I will have to check that for my application using scikit
learn. In R using the randomforest package, the results were pretty
much identical but my classes were balanced already, which I think is
one factor that can lead to significant differences between binary
classification probabilities vs regression.
It was a study by somebody else, I can't remember which one right now,
but it will come back to me. But yes, the fact that for species
distribution modeling the sampling is often highly unbalanced (with
large number of pseudo-absence) is likely to play a role.
Yes definitely will use this as a template to include other methods. I
Only recently switched my work from R to Python but am just submitting
a paper based on R which uses a range of classifiers like
randomforest, GLM, GAM, and MARS which it was useful to evaluate the
differences.
It sometimes seems there are almost as many different conclusions about
the best method as there are publications (OK, I might exaggerate a bit
here), so comparing difference models is very useful. So very glad you
are doing this (as I said, I have looked at scipy before and how it
could be implemented in GRASS, but my Python skills are just not up to it).
Steve
_____________________________
From: Paulo van Breugel <[email protected]
<mailto:[email protected]>>
Sent: Sunday, March 27, 2016 3:11 AM
Subject: Re: [GRASS-dev] RandomForest classifier for imagery groups add-on
To: Vaclav Petras <[email protected]
<mailto:[email protected]>>, Steven Pawley
<[email protected] <mailto:[email protected]>>
Cc: <[email protected] <mailto:[email protected]>>
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]> 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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