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

Laurent C. lrntct at gmail.com
Sat Mar 26 12:21:05 PDT 2016

Hi Steven.

Interesting. I'll try to have a look when I find some time.
Speaking of ROC, it made me think that I've written a small module
that calculate some scores for evaluating dichotomous forecasts:

If it can be of use to someone.


2016-03-26 10:40 GMT-06:00 Steven Pawley <dr.stevenpawley at gmail.com>:
> Hello developers,
> 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. Similar to existing GRASS classification methods, it uses an
> imagery group and a raster of labelled pixels as the inputs for the
> classification. It also reads the rasters row-by-row, and then bundles these
> rows based on a user specified row increment to the classifier to keep
> memory requirements low, but also allow efficient classification because the
> scikit-learn implementation is multithreaded by default, and row-by-row
> results in too much stop-start behaviour. The feature importance scores and
> out-of-bag error are displayed in the command window.
> I would appreciate testing - you need to have scikit-learn and pandas
> installed in your Python environment which is easy on Linux and OS X, and
> instructions are provided in the tool for Windows.
> I have another add-on that I will upload soon, r.roc, which generates ROC
> and AUROC for prediction models.
> Steve
> Sent from Outlook Mobile
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