similar to: Problem with RWeka-rJava packages

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, 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

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),

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

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 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

Muchas gracias Carlos, Me da error al hacerlo. Mi variable donde quiero que localice las palabras de la lista tiene más de una palabra, no se si puede ser por eso. Gracias El Mar, 22 de Mayo de 2018, 20:15, Carlos Ortega escribió: > Hola, > > Aquí tienes un ejemplo (reproducible)... > > #----------------------- >> # Generar nombres de mujer >> library(randNames)

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:

Your message seems unclear, and as evidence the respondents are giving various answers. You should provide a small sample of input and output data as it would look in R to avoid this kind of thrashing about. See [1][2][3] for guidance. Note that you also really need to figure out how to make sure your email program sends plain text, because HTML formatting WILL be stripped by the mailing list

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

Dear Forum, I have installed and used various Weka functions in R - both already available interfaces or created ones via make_Weka_classifier - without any trouble. However, the RBFNetwork (RBF Neural Network) function is one that I have not been able to call. I tried creating the R interface using RBF<- make_Weka_classifier("weka/classifiers/functions/RBFNetwork"), and the

Hello, I was wondering what would be the easiest way to append weights to a dataset in RWeka. Ideally, I'd like to have something like: m <- LogitBoost(Species ~ ., data=iris, weights = myweights) But that, as far as I understand, it is not implemented and I'd need to use a workaround. I know that when programming Weka in Java it is possible to assign weights to instances using

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"

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 =

I too have this problem. Everything worked fine last year, but after updating R and packages I can no longer do word stemming. Unfortunately, I didn't save the old binaries, otherwise I would just revert back. Hoping someone finds a solution for R on Windows. Thanks! There is a potential solution for R on Mac OS from Kurt Hornik copied below, but I cannot get this to work on Windows.

Cordial saludo, Estoy haciendo una grafica de la base de datos Cars93 conde relaciono Manufacturer con Price con este codigo library(MASS) with(Cars93,plot(Manufacturer, Price, las=2, ylab="Precio (en $1,000)")) necesito el comando para que queden ordenados de manera ascendente como en la grafica 2, muchas gracias por su ayuda Grafica 1 [image: M-P.JPG] Grafica 2 [image: M-P

Hi, I'm trying to fit a binary logistic regression model, and would like to consider certain characteristics B and C only for people with variable A=1 and not for those with variable A=0, so I'm trying to do the following: model<- lrm(formula= y ~ A: (B+C) + D + E +...) I've had no problem with adding interaction terms using " * ", but every time I try to use

Hello, i get some memory problems using RWeka and i'm wondering because i have only ~ 400MB in RObjects without a high memory consuming operation when a crash occur. Look into hs_err_pid.log , it seems that the VM max heap size is only 512 mb. VM Arguments: jvm_args: -Xmx512m vfprintf exit java_command: <unknown> Launcher Type: generic I try to change with