Changeset 15614 for branches/M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestModel.cs

Timestamp:

01/15/18 08:21:48 (6 years ago)

Author:

bwerth

Message:

#2847 made changes to M5 according to review comments

Location:

branches/M5Regression/HeuristicLab.Algorithms.DataAnalysis

Files:

• . (modified) (1 prop)
• 3.4 (modified) (1 prop)
• 3.4/RandomForest/RandomForestModel.cs (modified) (3 diffs)

branches/M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestModel.cs

@@ -288 +288 @@
     public static RandomForestModel CreateRegressionModel(IRegressionProblemData problemData, int nTrees, double r, double m, int seed,
       out double rmsError, out double outOfBagRmsError, out double avgRelError, out double outOfBagAvgRelError) {
-      return CreateRegressionModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed, out rmsError, out avgRelError, out outOfBagAvgRelError, out outOfBagRmsError);
+      return CreateRegressionModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed,
+       rmsError: out rmsError, outOfBagRmsError: out outOfBagRmsError, avgRelError: out avgRelError, outOfBagAvgRelError: out outOfBagAvgRelError);
     }
 
@@ -300 +301 @@
 
       rmsError = rep.rmserror;
+      outOfBagRmsError = rep.oobrmserror;
       avgRelError = rep.avgrelerror;
       outOfBagAvgRelError = rep.oobavgrelerror;
-      outOfBagRmsError = rep.oobrmserror;
 
       return new RandomForestModel(problemData.TargetVariable, dForest, seed, problemData, nTrees, r, m);
@@ -309 +310 @@
     public static RandomForestModel CreateClassificationModel(IClassificationProblemData problemData, int nTrees, double r, double m, int seed,
       out double rmsError, out double outOfBagRmsError, out double relClassificationError, out double outOfBagRelClassificationError) {
-      return CreateClassificationModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed, out rmsError, out outOfBagRmsError, out relClassificationError, out outOfBagRelClassificationError);
+      return CreateClassificationModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed, 
+        out rmsError, out outOfBagRmsError, out relClassificationError, out outOfBagRelClassificationError);
     }