"Binary2MultiClassLearner Bug?"
Hi,
After getting great service when I reported my KernelPCA bug (fixed in 4.5 it seems - thanks!), I thought I'd throw another out there that tripped me up before, and again tonight.
When using the meta learner Binary2MultiClassLearner, the inner learners can sometimes still complain about getting polynomial labels. Usually the first run of my XV will work, then the 2nd will fail - mostly this is with some other inner meta-learner (a booster / bagger etc).
So, taking the sample 24_Binary2MultiClassLearner.xml, in RM 4.5 and make a small modification to get the learner to be a BayesianBoosting->RuleLearner like so:
G Aug 1, 2009 12:02:50 AM: [Fatal] UserError occured in 7th application of BayesianBoosting (BayesianBoosting)
G Aug 1, 2009 12:02:50 AM: [Fatal] Process failed: This learning scheme does not have sufficient capabilities for the given data set: polynominal label not supported
Am I doing this correctly? Any help appreciated.
(To date I've actually been pre-processing my data to essentially do my own version of a 1-vs-all, but not having to go through a manual step would be great)
--Q
After getting great service when I reported my KernelPCA bug (fixed in 4.5 it seems - thanks!), I thought I'd throw another out there that tripped me up before, and again tonight.
When using the meta learner Binary2MultiClassLearner, the inner learners can sometimes still complain about getting polynomial labels. Usually the first run of my XV will work, then the 2nd will fail - mostly this is with some other inner meta-learner (a booster / bagger etc).
So, taking the sample 24_Binary2MultiClassLearner.xml, in RM 4.5 and make a small modification to get the learner to be a BayesianBoosting->RuleLearner like so:
<operator name="Root" class="Process" expanded="yes">I get the following message (although not on the first call):
<description text="#ylt#p#ygt#The meta learning schemes used in this setup is a binary to multi class converter. This allows the learning of model for polynominal data sets (i.e. for data sets with more than two classes) by learners supporting only binominal classes. #ylt#/p#ygt# "/>
<operator name="ExampleSource" class="ExampleSource">
<parameter key="attributes" value="../data/iris.aml"/>
</operator>
<operator name="XValidation" class="XValidation" expanded="yes">
<operator name="Binary2MultiClassLearner" class="Binary2MultiClassLearner" expanded="yes">
<operator name="BayesianBoosting" class="BayesianBoosting" expanded="yes">
<operator name="RuleLearner" class="RuleLearner">
<parameter key="pureness" value="0.6"/>
<parameter key="minimal_prune_benefit" value="0.1"/>
</operator>
</operator>
</operator>
<operator name="OperatorChain" class="OperatorChain" expanded="yes">
<operator name="ModelApplier" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="ClassificationPerformance" class="ClassificationPerformance">
<parameter key="classification_error" value="true"/>
<list key="class_weights">
</list>
</operator>
</operator>
</operator>
</operator>
G Aug 1, 2009 12:02:50 AM: [Fatal] UserError occured in 7th application of BayesianBoosting (BayesianBoosting)
G Aug 1, 2009 12:02:50 AM: [Fatal] Process failed: This learning scheme does not have sufficient capabilities for the given data set: polynominal label not supported
Am I doing this correctly? Any help appreciated.
(To date I've actually been pre-processing my data to essentially do my own version of a 1-vs-all, but not having to go through a manual step would be great)
--Q