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.
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.
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.
Steve
_____________________________
From: Paulo van Breugel <[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]>, Steven Pawley
<[email protected]>
Cc: <[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]>
Sent: Saturday, March 26, 2016 11:21 AM
Subject: Re: [GRASS-dev] RandomForest classifier for imagery groups add-on
To: Steven Pawley < [email protected]>
Cc: < [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 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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