search for: weka_control

Hello, I have some trouble in achieving the desired parametrisation for the weka classifier functions, using the package RWeka. The problem is, that the functions result=classifier(formula, data, subset, na.action, control = Weka_control(mycontrol)) do not seem to be manipulated by the mycontrol- arguments Perhaps this should be resepected via the handlers- argument , but the documentation in this regard is rather sparse. # ------------- Examples file =system.file("arff","iris.arff",package="...

...ing the RWeka package: 1.) First question: How can one perform a cross validation, -say 10fold- for a given data set and given model ? 2.) Second question What is the correct syntax for the parametrization of e.g. Kernel classifiers interface m1 <- SMO(Species ~ ., data = iris, control = Weka_control(K="weka.classifiers.functions.supportVector.RBFKernel",G=0.1)) m2 <- SMO(Species ~ ., data = iris, control = Weka_control(K="weka.classifiers.functions.supportVector.RBFKernel",G=1.0)) > m1 SMO Kernel used: RBF kernel: K(x,y) = e^-(0.01* <x-y,x-y>^2)...

Dear All, I am finding trouble trying to guild a Wrapper using random forest to evaluate the subsets: I do: nombi <- make_Weka_filter("weka/filters/supervised/attribute/AttributeSelection") datbin<- nombi(gene ~., data=X1X2X4X5W, control =Weka_control( S=list("weka.attributeSelection.GeneticSearch"), E=list("weka.attributeSelection.WrapperSubsetEval"),B = list("weka.classifiers.trees.RandomForest"))) I get the following error: Exception in thread "main" java.lang.Exception: Illegal options: -B weka.cla...

...eBayes. I have first created an interface for the classifier > with: > > AS<-make_Weka_classifier("weka/classifiers/meta/AttributeSelectedClassifier") > > And then I am trying to run the classifier with: > > nb.model<-AS(class~.,data=ex, > ? ? ? ? ?control=Weka_control( > ? ? ? ? ? ?E="weka.attributeSelection.CfsSubsetEval", > ? ? ? ? ? ?S="weka.attributeSelection.BestFirst -D 1", > ? ? ? ? ? ?W="weka.classifiers.bayes.NaiveBayes -D")) > > But now, I get an error saying: > > Error in .jcall(classifier, "V&...

Dear all, I am trying to construct a wrapper that uses random forest to evaluate the subsets using RWeka and when I do: nombi <- make_Weka_filter("weka/attributeSelection/WrapperSubsetEval") datbin<- nombi(gene ~., data=X1X2X3X4W, control =Weka_control( B = list("weka.classifiers.trees.RandomForest"))) I also have tried with an other induction algorithm: datbin<- nombi(gene ~., data=X1X2X3X4W, control =Weka_control( B=list("weka.classifiers.trees.j48.J48 -F 5 -T 0.01 -S 1 -- -C 0.25 -M 2"))) and in both cases I get...

...rough the NominalToBinary filter ("weka.filters.supervised.attribute.NominalToBinary"). But unfortunately I can`t apply the filter to my data. Here is my code: nombi <- make_Weka_filter("weka/filters/supervised/attribute/NominalToBinary") x2bin <- nombi(data=dat, control =Weka_control(N=TRUE, A=TRUE)) I didn't get an error message, but it still don't work. My nominal attribute is of class "factor". Maybe the problem has to do with the argument list. Argument list: (formula, data, subset, na.action, control = NULL) What is meant with the argument "formul...

...ids) <- nms : 'names' attribute [150] must be the same length as the vector [0] In addition: Warning message: In is.nan(class) : is.nan() applied to non-(list or vector) of type 'NULL' another example of such an error is: Code: library(RWeka) cl1 <- SimpleKMeans(iris[, -5], Weka_control(N = 3)) Error: Error in names(class_ids) <- nms : 'names' attribute [150] must be the same length as the vector [0] In addition: Warning message: In is.nan(class) : is.nan() applied to non-(list or vector) of type 'NULL' I even copied the last code from Package RWeka manual...

...I view the m object, the model seems to have been trained on a lot fewer rows than expected (10 vs 1586?), and the summary() command fails with the error I mentioned I haven't seen anyone run into. My guess is something is wrong with the specification of the training set, but when I add control=Weka_control(F=1) to specify only one fold, the end result is the same with the degenerate confusion matrix error. Is there some other way I should be forcing it to train on more rows? Is that issue related to not being able to generate a confusion matrix? > attach(q) > nrow(q) [1] 1586 > summary...

I am using the package "tm" for text-mining of abstracts and would like to use it to find instances of gene names that may contain white space. For instance "gene regulatory protein 1". The default behavior of tm is to parse this into 4 separate words, but I would like to use the class constructor "dictionary" to define phrases such as just mentioned. Is this

...fications in the cross-validation. However, If I use the same data in R > train.df <- read.arff("HWlrTrain.arff") using RWeka, like this: NB <- make_Weka_classifier("weka/classifiers/bayes/NaiveBayes") wNB <- NB(decision ~ ., data = train.df, + control = Weka_control(K = TRUE)) > summary(wNB) === Summary === Correctly Classified Instances 4437 78.0475 % Incorrectly Classified Instances 1248 21.9525 % Kappa statistic 0.4446 Mean absolute error 0.2679 Root mean squared erro...

Hi all, I have a package that is specific to a task I was repetitively using a few years ago. I now needed to run it again with new data. However I am told it was built with an older version or R and will not work. How can I tweak the package so it will run on 11.1? It was a one-off product and has not been maintained. Is there a way to "unpackage" it and repackage it to work? I