Changeset 6241 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs

Timestamp:

05/20/11 18:54:39 (13 years ago)

Author:

gkronber

Message:

#1473: implemented random forest wrapper for classification.

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs (modified) (2 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs

@@ -87 +87 @@
       Results.Add(new Result(RandomForestRegressionModelResultName, "The random forest regression solution.", solution));
       Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the random forest regression solution on the training set.", new DoubleValue(rmsError)));
-      Results.Add(new Result("Average relative error", "The average of relative errors of the random forest regression solution on the training set.", new DoubleValue(avgRelError)));
-      Results.Add(new Result("Root mean square error (out-of-bag)", "The out-of-bag root of the mean of squared errors of the random forest regression solution on the training set.", new DoubleValue(outOfBagRmsError)));
-      Results.Add(new Result("Average relative error (out-of-bag)", "The out-of-bag average of relative errors of the random forest regression solution on the training set.", new DoubleValue(outOfBagAvgRelError)));
+      Results.Add(new Result("Average relative error", "The average of relative errors of the random forest regression solution on the training set.", new PercentValue(avgRelError)));
+      Results.Add(new Result("Root mean square error (out-of-bag)", "The out-of-bag root of the mean of squared errors of the random forest regression solution.", new DoubleValue(outOfBagRmsError)));
+      Results.Add(new Result("Average relative error (out-of-bag)", "The out-of-bag average of relative errors of the random forest regression solution.", new PercentValue(outOfBagAvgRelError)));
     }
 
@@ -116 +116 @@
       outOfBagRmsError = rep.oobrmserror;
 
-      return new RandomForestRegressionSolution(problemData, new RandomForestRegressionModel(dforest, targetVariable, allowedInputVariables));
+      return new RandomForestRegressionSolution(problemData, new RandomForestModel(dforest, targetVariable, allowedInputVariables));
     }
     #endregion