[GRASS-SVN] r69661 - grass-addons/grass7/raster/r.randomforest

[svn_grass at osgeo.org]

Author: spawley
Date: 2016-10-03 14:53:14 -0700 (Mon, 03 Oct 2016)
New Revision: 69661

Modified:
   grass-addons/grass7/raster/r.randomforest/r.randomforest.html
Log:
Major update to the r.randomforest addon. Several other classifiers have been included. Also bug fixes to the ROC_AUC options which previously was just displaying classification accuracy

Modified: grass-addons/grass7/raster/r.randomforest/r.randomforest.html
===================================================================
--- grass-addons/grass7/raster/r.randomforest/r.randomforest.html	2016-10-03 21:51:25 UTC (rev 69660)
+++ grass-addons/grass7/raster/r.randomforest/r.randomforest.html	2016-10-03 21:53:14 UTC (rev 69661)
@@ -1,6 +1,6 @@
 <h2>DESCRIPTION</h2>
 
-<em><b>r.randomforest</b></em> represents a front-end to the scikit learn machine learning python package for the purpose of performing classification and regression on a suite of predictors within a GRASS imagery group. The module also provides access random forest classification, and several other classifiers that are commonly used in remote sensing and spatial modelling. For more information concerning the details of any of the algorithms, consult the scikit-learn documentation directly. The choice of classifier is set using the <i>model<i/> parameter.
+<em><b>r.randomforest</b></em> represents a front-end to the scikit learn machine learning python package for the purpose of performing classification and regression on a suite of predictors within a GRASS imagery group. The module also provides access random forest classification, and several other classifiers that are commonly used in remote sensing and spatial modelling. For more information concerning the details of any of the algorithms, consult the scikit-learn documentation directly. The choice of classifier is set using the <i>model</i> parameter.
 
 <br><br>The RandomForestsClassifier and RandomForestsRegressor (Breiman, 2001) options represent ensemble classification and regression tree methods, respectively. These methods construct a forest of uncorrelated decision trees based on a random subset of predictor variables, which occurs independently at every node split in each tree. Each tree produces a prediction probability, and the final classification result is obtained by averaging of the prediction probabilities across all of the trees. Random forests require relatively few user-specified parameter choices, principally consisting of the number of trees in the forest (<i>ntrees_rf</i>), and the number of variables that are allowed to be chosen from at each node split (<i>m_features_rf</i>), which controls the degree of correlation between the trees. Random forests also includes built-in accuracy assessment, termed the 'out-of-bag' (OOB) error. This is computed through bagging, where 33% of the training data are held-out duri
 ng the construction of each tree, and then OOB data are used to evaluate the prediction accuracy.