similar to: Questions on RWeka classifiers?

Hi, I am using some classifiers in RWeka packages and met a couple problems. (1) J48 implements C45 classifier, the C45 should be able to handle missing values in both training set and test set. But I found the J48 classifier can not be evaluated on test set with missing values--it just ignore them. (2) The ensemble classifiers in RWeka such as bagging and boosting: there is a

Hi All, I am trying to use the C50 package to build classification trees in R. Unfortunately there is not enought documentation around its use. Can anyone explain to me - how to prune the decision trees? Regards, Indrajit [[alternative HTML version deleted]]

Good afternoon. I would like to know if there is any function in R to do LOOCV with these classifiers: 1)SVM 2)Neural Networks 3)C4.5 ( J48) 4)KNN Thanks a lot! [[alternative HTML version deleted]]

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

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

Hello everybody I'm having some difficulties to design the decision tree algorithm J48. I am using the following code and when I run it gives me the following message plot(m1) Error in plot.Weka_tree(m1) : Plotting of trees with multi-way splits is currently not implemented. #The code library(RWeka) library(randomForest) library(party) if(require(mlbench, quietly = TRUE) &&

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

Dear All,I encountered a problem when I use RWeka for prediction. Specifically, I use the following: res=J48(X1~.,data=mydata); predict(res), #it worked fine but when I tried to use a different data set, i.e. predict(res,newdata=mynewdata); all the predictions I get is 0, which apparently is problematic. What is weird is, if I use the old data, but use the newdata option, i.e.

Hi, Being a novice this is my first usage of R. I am trying to use rpart for building a decision tree in R. And I have the following dataframe Outlook Temp Humidity Windy Class Sunny 75 70 Yes Play Sunny 80 90 Yes Don't Play Sunny 85 85 No Don't Play Sunny 72 95 No Don't Play Sunny 69 70 No Play Overcast 72 90 Yes Play Overcast 83 78 No Play Overcast 64 65 Yes Play Overcast 81 75

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

Hello, I would like to test some learning algorithms (C5.0, or C4.5) using decision trees or neural network. I didn't find anything in the R documentation for functions to apply on datasets (well, English is not my mother tongue and I have some problems to understand some functions descriptions). Does anybody know where I can find anything about that ? Thank you very much, Thomas

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,

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:

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

I am using machine learning for one researching. I am using some classifiers with 5-fold CV . I would like to know how it is possible to extract the accuracy, for example, for KNN,neural networks and J48, for each one of 5-fold because when I apply CV to my classifier, I obtain the "mean accuracy" of 5-fold but each accuracy/error of each fold is not returned. Any help is welcome and

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, 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 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

Hola, ¿cómo están? Soy estudiante de Ing. en Sistemas de Información y estoy cursando la materia Tecnologías para la Explotación de Datos. Me he decidido a usar R para hacer DM. Estoy recién comenzando con ambos. El asunto es que no he encontrado la implementación en R de uno de los algoritmos que dimos para árboles de decisión, ID3. Ya revisé varios de los paquetes para DM

Dear R-helpers: I am interested in discretization methods for numerical attributes, as they are reported in the 'machine learning' community. For example, the work of Fayyad & Irani (IJCAI-93), Kononenko, entropy-based approaches, MDL principle, the C4.5 approach, etc. I am especially interested in those methods that take a factor as goal target into account for discretizing