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: https://bitbucket.org/lrntct/r.sim.stats/
If it can be of use to someone. Regards, Laurent 2016-03-26 10:40 GMT-06:00 Steven Pawley <[email protected]>: > 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 > > > _______________________________________________ > grass-dev mailing list > [email protected] > http://lists.osgeo.org/mailman/listinfo/grass-dev _______________________________________________ grass-dev mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-dev
