Now I doing my thesis and i use formular of Root relative square error :rrse for calculate
in my program. But I calculate result rrse not similar rrse that calculate from weka program
who can tell formular rrse?
I read data mining of Ian H. Witten and Eibe Frank that have formular of rrse
but i can't understand valiable
RRSE = I attach formular file "Now I doing my thesis ..."
I am not sure that p1,….,,pn , a1,….,,an and a bar
Suppose have 2 test data each data have 2 class when calculate from Naïve Bayes each test data has probability
Class 1 Class 2
Test data 1 0.9 0.1
Test data 1 0.8 0.2
Value of p1,….,,pn , a1,….,,an and a bar=?
And RRSE =?
Please Tell me I don’t understand
Thank You for Answer
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Now I have the rule from apriori m_allTheRules. how i compare each tuple
(test dataset) with FastVector m_allTheRules?
I 'm create myClassifier (associative Classification) , I want to create
confusion matric from comparing.
||| m_allTheRules <===> Instances test |||
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On a classification problem i had, i tested the option of "Preserve order for %
However i noticed a significant drop between the observed accuracy between the
training set and the test set. 10-fold cross validation gave me accuracy around
90%. Running the same dataset 10-times using a 66% training sample by using
different seed values gave me an average of around 91%. When i tested the
"preserve order" option the accuracy on the test set dropped to 75% (...!)
After that i used 2 other datasets that are included in the weka distribution
and i had the same result : The results on the test set were much inferior when
using the "preserve order" option rather than anything else. Has anyone
experienced the same problem? Am i missing something here?
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Content preview: I cannot understand what's the meaning of the
Cross-validation option in the "select attributes" tab of the
Explorer. What I want to do is: a Backward elimination (with
GreedyStepwise) evaluated with a classifier (ClassifierSubsetEval);
i.e. I want to try eliminating one attribute per time, then perform a
cross-validation, and if the accuracy is better, permanently eliminate
that attribute and proceed with another elimination. BUT: in the
ClassifierSubsetEval Editor I can only choose beetwen holding out a
test set or using training set. The Cross-validation option is not in
the classifier editor but directly in the "selct attributes" tab. And
this is what I don't understand. What the meaning of a
cross-validation evaluated with, for example, with the
ReliefAttributeEval? Doesn't it have sense only with Classifier
Evaluation? So why it's an external option, and not in its editor? For
my purpose (I've axplained it just above), should i check the
Cross-validation option, and choose "use training" in the
ClassifierSubsetEval? thanx Thomas [...]
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> I made n-number of clusters from a dataset using WEKA-3-5, where n<< number
> of instances in the dataset.
> The size of the dataset is 144 attributes and 2001 instances.
> Now, I want to know which instance fall into which cluster. How can I do
> that with WEKA?
Use the "clusterInstance(Instance)" method of the
weka.clusterers.Clusterer class. Also, see section "Clustering" of the
following Wiki article for more information on using the clusterer API:
If you don't want to work on the code level, use one of the following
filters (see corresponding Javadoc for more information):
Peter Reutemann, Dept. of Computer Science, University of Waikato, NZ
http://www.cs.waikato.ac.nz/~fracpete/ +64 (7) 838-4466 Ext. 5174
>You're, If I'm not mistaken, the second person using the -xml option that I've
Count me in too. In fact, I have just recommended it to a colleague who uses
meta-classifiers with other meta-classifiers. It is far easier than the
command-line with options that span several lines!
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I'm trying to set delimiters for WordTokenizer as a parameter to
StringToWordVector which is in turn a parameter to FilteredClassifier.
I have not been able to do this using a command line or an xml file for
the options. This command line works without the -delimiters flag:
$ java -cp "c:\incoming\weka\developer-branch\weka.jar"
"weka.filters.unsupervised.attribute.StringToWordVector -C -L -T -I -S
-W 2000 -N 1 -tokenizer \"weka.core.tokenizers.WordTokenizer\"" -W
"weka.classifiers.bayes.NaiveBayesMultinomial" -t test.arff
How would you add "-delimiters <delimiter_string>" here to the value for
-tokenizer, e.g. if the delimiter string is " \\t\\r\\n