similar to: Why use numFold in evaluate_Weka_classifier of RWeka

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

Hi, First of all, I'm a complete rookie to R (~2 weeks). But anyway, I'm trying to use the RWeka interface for C4.5 (J48) classification. As a proof of concept I'm using the Iris data set to create a training set of 30 instances (10 per species) and use the remaining 120 instances as my test set. This is what I do: trainingIndices <- rep(1:10, 3) + rep(0:2, each=10) * 50

Hi everyone, I have a test set with more than 1000 cases, when I use evaluate_Weka_classifier(RWeka)to evaluate my classifier on this test set, the output shows me the result of only 83 cases. I do have missing values in predictors, so I tried na.acton=na.pass, but it dosen't help. Now I confused, why RWeka ignore so many cases in my test set? Is there any setting I didn't notice in

Hi all, Although I sent the mail to Piotr, the author of FSelector, it should be better to ask here to let others know. Yanwei Begin forwarded message: From: Yanwei Song <yanwei.song@gmail.com> Date: September 7, 2011 4:41:58 PM EDT To: p.romanski@stud.elka.pw.edu.pl Subject: FSelector and RWeka problem Dear Piotr, Thanks for developing the FSelector package for us. I'm a new

Hi, Everyone, I just installed R-2.7.2 on my computer and then installed package RWeka, version 0.3-13. I noticed that when using command "evaluate_Weka_Classifier", with parameter "newdata=", it still evaluated on training data. Does anyone else noticed this? My older version of R-2.6.1 with RWeka 0.3-9 worked fine on the same computer. Bin

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),

I'm a big fan of both Weka and R (quite new at R :) ), and jumped at the chance to use them together. Unfortunately, I'm running into what is probably a dumb error when trying to view info about my model. A Google search turned up 0 hits for the actual error I got (last line), but you all are smarter! My code is below, but basically my data frame (q) is imported via RODBC and has 1586

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

Hi, Does anyone know what the negative indexing of a matrix mean? I am using the RWeka and this evaluate classifier does not work on new data like this e <- evaluate_Weka_classifier(m1235,newdata=XW4, complexity = FALSE,class = FALSE) while it work with negative indexing: e <- evaluate_Weka_classifier(m1235,newdata=XW4[,-2], complexity = FALSE,class = FALSE) Although I

Hi, Does anyone know what the negative indexing of a matrix mean? I am using the RWeka and this evaluate classifier does not work on new data like this e <- evaluate_Weka_classifier(m1235,newdata=XW4, complexity = FALSE,class = FALSE) while it work with negative indexing: e <- evaluate_Weka_classifier(m1235,newdata=XW4[,-2], complexity = FALSE,class = FALSE) Although I

Hola a todos, He echado un vistazo a alguno de los paquetes de R que incluyen clasificadores y no sonsigo encontrar ninguno que tenga alguna función para usar un clasificador de maxima probabilidad tradicional. Alquien sabe de alguna? Gracias Víctor. [[alternative HTML version deleted]]

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

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"

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

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

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 have a question to ask. if in a linear regression model, the independent variables are not statistically significant, is it necessary to test these variables in a non-linear model? Since most of non-linear form of a variable can be represented to a linear combination using Taylor's theorem, so I wonder whether the non-linear form is also not statistically significant in such a

lib/CodeGen/RegAllocLocal.cpp:38: warning: 'NumFolded' defined but not used This has been introduced because of r46821. However, maybe removing just the variable isn't enought, because the comments in the section that got modified by 46821 are not optimal: if (PhysReg) { // Register is available, allocate it! assignVirtToPhysReg(VirtReg, PhysReg); } else { // No

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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