similar to: RWeka and Back Propagation NN

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

Hello, I have two questions concerning 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 =

Running Weka's command line with calls to system(), like this > system("java weka.classifiers.bayes.NaiveBayes -K -t HWlrTrain.arff -o") === Confusion Matrix === a b <-- classified as 3518 597 | a = NoSpray 644 926 | b = Spray === Stratified cross-validation === === Confusion Matrix === a b <-- classified as 3512 603 | a = NoSpray

Hello all, I am attempting to run an R script that makes use of RWeka. I am running SuSE Linux 9.3 with Java 1.5.0_06, R version 2.2.1, Weka 3-4-7, and Rweka 0.2-1. CLASSPATH="/usr/local/weka-3-4-7/weka.jar:/usr/local/JGR/JGR.jar" I receive the error: NewObject("weka/core/Instances","(Ljava/io/Reader;)V",...) failed Exception in thread "main"

Le jeudi 09 f?vrier 2012 ? 15:31 +0200, Kari Ruohonen a ?crit : > Hi, > I am trying to replicate a training of AttributeSelectedClassifier with > CFsSubsetEval, BestFirst and NaiveBayes that I have initially done with > Weka. Now, I am trying to use RWeka in R. > > I have a problem of passing arguments to the CfsSubsetEval, BestFirst > and NaiveBayes. I have first created an

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

Hi everyone, I have trouble to use RWeka, I tried: (w=weather dataset, all preditors are nominal) > m<-J48(play~., data=w) > e<-evaluate_Weka_classifier(m,cost = matrix(c(0,2,1,0), + ncol = 2),numFolds = 10, complexity = TRUE,seed = 123, + class = TRUE) it gives me exactly what I want, but when I tried the same classifier on the other published data: (iris dataset has all numeric

Dear List, I´m trying to implement the functionalities from WEKA into my modeling project in R through the RWeka package. In this context I have a slightly special question about the filters implemented in WEKA. I want to convert nominal attributes with k values into k binary attributes through the NominalToBinary filter ("weka.filters.supervised.attribute.NominalToBinary"). But

Hi, I'm trying to use Information Gain for feature selection. There is a InfoGain implementation in Weka: *weka.attributeSelection.InfoGainAttributeEval* Is it possible to use this function with RWeka? If yes how? list_Weka_interfaces doesn't show it and there is no make function for AttributeEvaluators. Is there any other implementation of InformationGain in R? Thank you Michael

Hello: I´m trying to execute Apriori(file.arff) command of RWeka package. I´m working with: Operating System: Windows XP home R-2.4.0 RWeka_0.2-11 rJava_0.4-11 classpath= .;C:\Archivos de programa\Java\jdk1.5.0\lib;C:\Archivos de programa\R\R- 2.4.0\library\RWeka\jar An error occurs when .jnew command is executed, on class "weka/core/Instances" :

Hi everyone, I am new to field of data mining as well as particularly using R respectively RWeka for writing my master thesis. I intend to create some specific J48 classification trees with the RWeka_classifiers_tree function. When I run the source code it says ?cannot handle numeric class?. I therefore checked the arff-file and indeed there it says that the class variable is numeric although it

Hello, I have a question concerning decision trees coming from RWeka : library(RWeka) m =J48(Species~.,data=iris) How could such a decision tree be transferred into a matrix, pretty much in the same fashion, as it is done by getTree() in library(ofw) library(ofw) data(srbct) attach(srbct) ##ofwCART learn.cart.keep <- ofw(srbct,

Hi, I want to save a RWeka model into a file, in order to retrive it latter with a load function. See this example: library(RWeka) NB <- make_Weka_classifier("weka/classifiers/bayes/NaiveBayes") model<-NB(formula,data=data,...) # does not run but you get the idea save(model,file="model.dat") # simple save R command # ... load("model.dat") # load the model

Saludos colegas. Estoy intentando cargar un archivo arff con el paquete RWeka pero me lanza el error FileNotFoundException cuando intento ejecutar la instrucción en R. Este es el código: datos<- read.arff(system.file("arff","cereals.arff", package = "RWeka")) El archivo cereals.arff y el script con el código R están en el mismo directorio, sin embargo, no me

Hi, I am trying to read in a large and highly sparse ARFF file into R which was produced by WEKA. However the package 'RWeka' just chokes on this file. The data set has about 40k observations and about 20k dimensions. Even after 1hr read.arff method of RWeka is still trying to read in the file, whereas WEKA is able to read it in in less than 20seconds. What are my options at this

Hi I'm trying to use RWeka to use a NaiveBayes Classifier(the Weka version). However it crashes whenever there is a NA in the class Gender Here is the.code I have with d2 as the data frame. The first call to NB doesn't make R crash but the second call does. NB <- make_Weka_classifier("weka/classifiers/bayes/NaiveBayesSimple") d2[,64]<-d2$Gender=="M"

Hi, I'm trying to use RWeka and followed the following example from the RWeka manual. ============ ## Use some example data. w <- read.arff(system.file("arff","weather.nominal.arff", package = "RWeka")) ## Identify a decision tree. m <- J48(play~., data = w) m ## Use 10 fold cross-validation. e <- evaluate_Weka_classifier(m, cost = matrix(c(0,2,1,0),

Dear All, I'd like to use Weka functions that are not implemented (do not have interface) in RWeka, like the Remove function and others in the future! The .java() functionality is for that purpose but I haven't seen any example with Weka functions. Could anyone give me hand in how to do it? For instace if I want to use the weka.filters.unsupervised.attribute.Remove? 1. in the R console,

Hi, I downloaded RWeka successfully. (At least I do not see, where I could have made a mistake.) Then I tried to load it by the library-command. To my surprise this did not work. Result: library(RWeka) Fehler in .jinit(c(system.file("jar", "weka.jar", package = "RWeka"), system.file("jar", : Cannot create Java Virtual Machine Fehler: .onLoad in

Hi - I have a question on RWeka. I installed the package and try to run using some examples available in the package. However, it stalls my machine for a while. I'm wondering if I need weka (which is java implementation) installed before using RWeka? Thank you. - adschai