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

Steven Pawley dr.stevenpawley at gmail.com
Sat Mar 26 09:40:09 PDT 2016

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.
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