similar to: [klaR package] [NaiveBayes] warning message numerical 0 probability

Hello, We have a system which creates thousands of regression/classification models and in cases where we have only one input variable NaiveBayes throws an error. Maybe I am mistaken and I shouldn't expect to have a model with only one input variable. We use R version 2.6.0 (2007-10-03). We use caret (v4.1.19), but have tested similar code with klaR (v.0.5.8), because caret relies on

Hi all, I run R 2.11.1 under ubuntu 10.10 and caret version 2.88. I use the caret package to compare different models on a dataset. In order to compare their different performances I would like to use the same data partitions for every models. I understand that using a LGOCV or a boot type re-sampling method along with the "index" argument of the trainControl function, one is able to

Hi, I have written a code in R for classifying microarray data using naive bayes, the code is given below: library(e1071) train<-read.table("Z:/Documents/train.txt",header=T); test<-read.table("Z:/Documents/test.txt",header=T); cl <- c(c(rep("ALL",10), rep("AML",10))); cl <- factor(cl) model <- NaiveBayes(train,cl);

Hi This is a question regarding classification performance using different methods. So far I've tried NaiveBayes (klaR package), svm (e1071) package and randomForest (randomForest). What has puzzled me is that randomForest seems to perform far better (32% classification error) than svm and NaiveBayes, which have similar classification errors (45%, 48% respectively). A similar difference in

Hi, I'm currently using the R package e1071 to train naive bayes classifiers and came across a bug: When the posterior probabilities of all classes are small, the result from the predict.naiveBayes function become NaNs. This is an issue with the treatment of the log-transformed probabilities inside the predict.naiveBayes function. Here is an example to demonstrate the problem (you might need

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"

I did this: nb <- naiveBayes(users, platform) pl <- predict(nb,users) nrow(users) ==> 314781 ncol(users) ==> 109 1. naiveBayes() was quite fast (~20 seconds), while predict() was slow (tens of minutes). why? 2. the predict results were completely off the mark (quite the opposite of the expected overfitting). suffice it to show the tables: pl: android blackberry ipad

I'm trying to fit a naive Bayes model and predict on a new data set using the functions naivebayes and predict (package = e1071). R version 2.5.1 on a Linux machine My data set looks like this. "class" is the response and k1 - k3 are the independent variables. All of them are factors. The response has 52 levels and k1 - k3 have 2-6 levels. I have about 9,300 independent variables

hi there!! i am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas?? i am very glad about any help!! i need a naive bayes with 10-fold cross validation: code: library(e1071) model <- naiveBayes(code ~ ., mydata) tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min, sampling = c("cross"),

Hey, I just like to know how to extract details from the naiveBayes model (package e1071). I mean, for each possible value the model defines how much it influences the outcome. I want to sort those probabilities and show the values with the highest impact. How could I do that? PS: I tried using []'s to get to the model's internals, however, all I get is a "list" not a

My data is 50,000 instances of about 200 predictor values, and for all 50,000 examples I have the actual class labels (binary). The data is quite unbalanced with about 10% or less of the examples having a positive outcome and the remainder, of course, negative. Nothing suggests the data has any order, and it doesn't appear to have any, so I've pulled the first 30,000 examples to use as

I use Windows, R version 2.5.1 When I try to run stepclass (klaR) I get an error message/warning saying: 1: error(s) in modeling/prediction step in: cv.rate(vars = c(model, tryvar), data = data, grouping = grouping, ... Actually, I look 16 warnings of this type. Can anyone tell me what this means? Also, it returns only 2 out of the 79 variables as important, however these variables

Dear all, I am trying to do a n-fold cross-validation for a regularized discrimant function analysis using rda from the package klaR. However, I have problems to predict the groups from the test/validation sample. The exmaples of the R documantation and some online webpage also do not work. Does anybody know what I have done wrong? Here my code # I want to use the first 6 observations for

Dear all, I have a question on the shardsplot package:klaR(see the below Example). Plese tell me the meanings of " logstand <- t((t(logcount) / sdlogcount) * c(1,2,6,5,5,3))", much more. Why does this example use "c(1,2,6,5,5,3)" ? Examples: # Compute clusters and an Eight Directions Arranged Map for the # country data. Plotting the result.

Dear all, I tried a simple naive Bayes classification on an artificial dataset, but I have troubles getting the predict function to work with the type="class" specification. With type= "raw", it works perfectly, but with type="class" I get following error : Error in as.vector(x, mode) : invalid 'mode' argument Data : mixture.train is a training set with 100

Hi, a colleague ran a stepwise discriminant analysis twice in a row and got different results, suggesting some "sochasticity" in the algorithms involved. I looked at her data and found that there was a lot of collinearity, so that I reckoned that maybe "stepclass" (klaR) cannot find a clear winner when trying to include a new variable and makes a random choice. Is that true?

Sorry, I forgot to mention that I used the package "klaR". > > Dear all, I am trying to do a n-fold cross-validation for a > regularized discrimant function analysis using rda from the package > klaR. However, I have problems to predict the groups from the > test/validation sample. The exmaples of the R documantation and > some online webpage also do not work.

Hi, I'm trying to use "stepclass" to do a stepwise variable selection with method=lda. I keep getting this warning message, which shows up once for each variable added to the model during variable selection: Warning message: error(s) in modeling/prediction step in: cv.rate(vars = c(model, tryvar), data = data, grouping = grouping, I don't know how to interpret this warning. I

Hi ! Right now I am learning to use svm functions available in R and trying to use these function with given example. I was stuck with svmlight function which is available in klaR package. Any help would be appreciated regarding this function. 1. I am unable to use svmlight( ) which is available in package: klaR. Although I have downloaded klaR_0.6-3 package from

Hallo! I would need a code for 10-fold cross validation for the classifiers Naive Bayes and svm (e1071) package. Has there already been done something like that? I tried to do it myself by applying the tune function first: library(e1071) tune.control <- tune.control(random =F, nrepeat=1, repeat.aggregate=min.,sampling=c("cross"),sampling.aggregate=mean, cross=10, best.model=T,