Sorry if this is a newbie question, but what algorithms in Weka support
predicting a continuous variable? I'm new to data mining, so I'm not sure
what the formal terminology is. Going through the docs, it looks like nearly
all the algorithms are intended for classification or assigning a discrete
label to an observation, not predicting a continuous variable.
I was using weka (development branch) for some experiments when I ran across
the issue of sql statements not working properly, the issue was it was using
column names instead of column labels, this causes problems when you use
multiple columns with the same name or the same column multiple times.
I found the issue and fixed it in the source and have been using the working
ResultSetHelper.java line 103 now reads: m_ColumnNames[i - 1] =
InstanceQuery.java line 387 now reads:
columnNames.add(md.getColumnLabel(i + 1));
Actually I am using Weka in my code and doing a simple call to SVM
LibSVM svm = new LibSVM();
I am using Eclipse, it automatically adds import weka.classifiers.functions.LibSVM; I also removed LibSVM.jar from classpath but of no avail.
I also tried to run the code through command line. The compilation was Okay but running the code is giving same error that libsvm classes not in classpath. I could not figure out how weka.Run can be used in this case?
I am having slight confusion using weka.Evaluation framework.I am using
contact-lens.arff.My code is as follows
//clens = load arff(contact-lens.arff ....)
Evaluation evaluation = new Evaluation(cles);
J48 j48 = new J48();
evaluation.crossValidateModel(new J48(), clens, 3,
//print stastiscs using evaluation.printStats(..)..
I am having trouble with the random seed value. If I change the value the
value 7,8,9 I get different value for ROC area , RMS error and few other. Is
it usual? In my opinion this value must be constant despite random seed
value. Can you please explain me if I am missing anything ? This case
applies to other algorithm too (I tested ID3 and NN).
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