Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/eval/AbstractWekaEvaluation.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/eval/AbstractWekaEvaluation.java	(revision 4)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/eval/AbstractWekaEvaluation.java	(revision 5)
@@ -95,4 +95,8 @@
 				output.append(",tn_" + ((WekaCompatibleTrainer) trainer).getName());
 				output.append(",fp_" + ((WekaCompatibleTrainer) trainer).getName());
+				output.append(",trainerror_" + ((WekaCompatibleTrainer) trainer).getName());
+				output.append(",trainrecall_" + ((WekaCompatibleTrainer) trainer).getName());
+				output.append(",trainprecision_" + ((WekaCompatibleTrainer) trainer).getName());
+				output.append(",trainsuccHe_" + ((WekaCompatibleTrainer) trainer).getName());
 			}
 			output.append(StringTools.ENDLINE);
@@ -104,6 +108,8 @@
 		
 		Evaluation eval = null;
+		Evaluation evalTrain = null;
 		for( Classifier classifier : classifiers ) {
 			eval = createEvaluator(testdata, classifier);
+			evalTrain = createEvaluator(traindata, classifier);
 			
 			double pf = eval.numFalsePositives(1)/(eval.numFalsePositives(1)+eval.numTrueNegatives(1));
@@ -150,4 +156,12 @@
 			output.append("," + eval.numTrueNegatives(1));
 			output.append("," + eval.numFalsePositives(1));
+			output.append("," + evalTrain.errorRate());
+			output.append("," + evalTrain.recall(1));
+			output.append("," + evalTrain.precision(1));
+			if( evalTrain.recall(1)>=0.7 && evalTrain.precision(1) >= 0.5 ) {
+				output.append(",1");
+			} else {
+				output.append(",0");
+			}
 		}