# HG changeset patch
# User DaveM
# Date 1486664084 0
# Node ID 7848d183c7ab689ce1541643ff5f08424af8a02b
# Parent  7ec9bd8df11100416e4971001e67d1713a9dc4db
fixing comments

diff -r 7ec9bd8df111 -r 7848d183c7ab phase2/rfFeatureSelection.m
--- a/phase2/rfFeatureSelection.m	Thu Feb 09 18:11:51 2017 +0000
+++ b/phase2/rfFeatureSelection.m	Thu Feb 09 18:14:44 2017 +0000
@@ -7,16 +7,23 @@
 % labels is the set of labels for the data
 % numFeatures is the dimension of the output vector (default 5)
 % iterMethod is the method for which the features are cut down
-%       'onePass' will simply select the top (numFeatures) features and
-%       report them
-%       'cutX' will iteratively cut the bottom X percent of features out,
-%       and perform random forest feature selection on the new set, until
-%       the desired number of features has been returned
-%       'oobErr' will do something with the out-of-bag error, and return
+%       * 'onePass' will simply select the top (numFeatures) features and
+%       report them 
+%       * 'cutX' will iteratively cut the bottom X percent of
+%       features out, and perform random forest feature selection on the
+%       new set, until the desired number of features has been returned
+%       * 'oobErr' will do something with the out-of-bag error, and return
 %       that in some way, but this has not been implemented yet.
-%       'featureDeltaErr' will do something with the feature importance 
-%       prediction error, and return that in some way, but this has not 
-%       been implemented yet.
+%       * 'featureDeltaErr' will do something with the feature importance
+%       prediction error, and return that in some way, but this has not
+%       been implemented yet. The OOBPermutedVarDeltaError property is a
+%       numeric array of size 1-by-Nvars containing a measure of importance
+%       for each predictor variable (feature). For any variable, the
+%       measure is the increase in prediction error if the values of that
+%       variable are permuted across the out-of-bag observations. This
+%       measure is computed for every tree, then averaged over the entire
+%       ensemble and divided by the standard deviation over the entire
+%       ensemble.
 
 if(length(labels) ~= size(data,1))
     error('labels and data do not match up');
@@ -57,6 +64,7 @@
 	features = features(1:numFeatures);
 elseif(strcmp(iterMethod,'featureDeltaErr'))
     warning('This method has not been implemented yet, using onePass to return results')
+    % this will use variable FI
 	features = features(1:numFeatures);
 end
 end
\ No newline at end of file