weka

I want to use LibSVM classifier with Weka in my application. How can I (or where can I find good examples to) do this? andrew A little late now, surely, but I'll answer anyways. You have to use weka.jar, libsvm.jar, and wlsvm.jar (the libsvm wrapper) in your project. So just include all 3 jars in your build path or class path or whatever. You can get the wlsvm.jar from here: http://ailab.ist.psu.edu/yasser/wlsvm.html You can get weka from here: http://www.cs.waikato.ac.nz/ml/weka/ And you can get libsvm from here: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ I could not get this to work with weka

I am working on Weka and need to output the predication values (probabilities) of each labels for each test instance. In GUI there is an option in classify tab as (classify -> options -> Output predicted value) which does this work by outputting the prediction probabilities for each label but how to do this in java code. I want to receive probability scores for each label after classifying it ? The following code takes in a set of training instances, and outputs the predicted probability for a specific instance. import weka.classifiers.trees.J48; import weka.core.Instances; public class Main {

I've always thought from what I read that cross validation is performed like this: In k-fold cross-validation, the original sample is randomly partitioned into k subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k − 1 subsamples are used as training data. The cross-validation process is then repeated k times (the folds), with each of the k subsamples used exactly once as the validation data. The k results from the folds then can be averaged (or otherwise combined) to produce a single estimation So k models are built