cross validation result workspace [RM5]
Maven
Hi guys,
谢谢你的伟大的工作。我真的很喜欢新的user-friendly interface. Its really a great step forward for non experts in data mining to use your tool.
我想两个神经网络(hopefull训练y the standard neural net is backpropagation and RBF from weka). I tried to adopt the online tutorial / video for measuring performance using cross validation.
I have several questions to that output which is presented in the result workspace.
1.
Although I only have two X-validation processes I have 3 tabs PerformanceVector results. And I am not sure why it is 3 and not 2. Can you help me with that? I am using only the gui for the process building and was thinking I created two exact copies of the cross validation processes where I only substituted the learner.
2. The performance Vector tab says
"root_mean_squared_error: 5.810 +/- 1.100 (mikro: 5.905 +/- 0.000)"
Which I think is pretty high for the prediction error.... anyway what does the does the information in the brackets mean? (Sorry I am not an expert in learning machines and not familiar with the notation)
In the log file it appears a strange warning: "Feb 28, 2010 7:54:11 PM WARNING: Caught exception in concurrent execution of Perf RBF (inner) (Performance (Regression)): com.rapidminer.operator.UserError: Input example set does not have a predicted label attribute"
谢谢你的伟大的工作。我真的很喜欢新的user-friendly interface. Its really a great step forward for non experts in data mining to use your tool.
我想两个神经网络(hopefull训练y the standard neural net is backpropagation and RBF from weka). I tried to adopt the online tutorial / video for measuring performance using cross validation.
I have several questions to that output which is presented in the result workspace.
1.
Although I only have two X-validation processes I have 3 tabs PerformanceVector results. And I am not sure why it is 3 and not 2. Can you help me with that? I am using only the gui for the process building and was thinking I created two exact copies of the cross validation processes where I only substituted the learner.
2. The performance Vector tab says
"root_mean_squared_error: 5.810 +/- 1.100 (mikro: 5.905 +/- 0.000)"
Which I think is pretty high for the prediction error.... anyway what does the does the information in the brackets mean? (Sorry I am not an expert in learning machines and not familiar with the notation)
In the log file it appears a strange warning: "Feb 28, 2010 7:54:11 PM WARNING: Caught exception in concurrent execution of Perf RBF (inner) (Performance (Regression)): com.rapidminer.operator.UserError: Input example set does not have a predicted label attribute"
<参数键= " invert_filter " value = " true " / >
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Answers
thank you for this kind words.
The first problem does not occur on my side. I receive two PerformanceVectors, each of them containing three performance criterions.
To your second question: I don't know if a value of 5.8 is high or not? Depends very much on the scale of the label attribute, does it?
We distinguish between the macro and mikro average and variance. The first is the result, if the performance vectors of each XValidation fold are averaged with the same weight. The mikro average takes the number of examples into account, that was used to build the performance vector delivered to the XValidation: Folds with a higher number of examples receive increased weight. I hope this clarifies this a bit?
The log file entry does not appear on my side. Sorry for that
Greetings,
Sebastian
Maybe it has something to do with the feature of disabling old result tabs. Although I think I set the default to always delete old results.
I ll try again the process when I restart rapidminer next time ...
this result seems to remain there from a debug point since it's the direct result from the inner performance evaluator?
Greetings,
Sebastian