similar to: Bug in predict function for naiveBayes?

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

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, 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, I have a small doubt regarding naive Bayes. I am able to classify the data's properly but i am just stuck up with how to get the probability values for naive bayes. In the case of SVM we have "attr" function that helps in displaying the probability values. Is there any function similar to "attr" in naive Bayes that can be used for displaying the attribute values. my

Dear r-users, I am looking for an implementation of the Naive Bayes classifier for a multi-class classification problem. I can not even find the Naive Bayes classifier for two classes, though I can not believe it is not available. Can anyone help me? Uschi -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi! I'm using GLM, LDA and NaiveBayes for binomial classification. My training set is 70 rows long with 32 features, and my test set is 30 rows long with 32 features. Using Naive Bayes, I can train a model, and then predict the test set with it like so: ass4q1.dLDA = lda(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:3]) table(predict(ass4q1.dNB, ass4q1.testSetDF[,2:3]), ass4q1.testSetDF[,1])

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"),

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, Currently I testing the packets that contain built-in features for classification. Actually I looked packages such as: e1071, Klar, Caret, CORElearn. However, from what I noticed when building a naive Bayesian classifier, that they package use of the finite mixture model to estimate P (x | C) and using a normal distribution. In my research I use binary data and I want modeled P (x | C), eg the

Hey guys, I can't get my Naive Bayes model to predict. Forgive me if its simple... I've tried about everything and can't get it to work. Reproduceable code below. Thank you, Mike -- Michael Schumacher Manager Data & Analytics - ValueClick mike.schumacher@gmail.com * Functional Example Code from UCLA:

Hey, I am confused about how to generate the sample data from a mixture of Multivariate Gaussian ditribution. For example, there are 2 component Gaussian with prior probability of 0.4 and 0.6, the means and variances are u1=[1 1]', Cov1=[1 0;0 1] and u2=[-1 -1]', Cov2=[1 0;0 1] repectively. So how can I generate a sample of 500 data from the above mixture distribution? Thanks. Fred

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 run R 2.10.1 under ubuntu 10.04 LTS (Lucid Lynx) and klaR version 0.6-4. I compute a model over a 2 classes dataset (composed of 700 examples). To that aim, I use the function NaiveBayes provided in the package klaR. When I then use the prediction function : predict(my_model, new_data). I get the following warning : "In FUN(1:747[[747L]], ...) : Numerical 0 probability with

Hi all, For some odd reason when running na?ve bayes, k-NN, etc., I get slightly different results (e.g., error rates, classification probabilities) from run to run even though I am using the same random seed. Nothing else (input-wise) is changing, but my results are somewhat different from run to run. The only randomness should be in the partitioning, and I have set the seed before this

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

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"

In case you don't get an answer from someone more knowledgeable: 1. I don't know. 2. But it is possible that other packages that are loaded after set.seed() fool with the RNG. 3. So I would call set.seed just before you invoke each random number generation to be safe. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking

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

Dear List, I am looking for a possibility to fit a mixture model under R using maximum likelihood estimation. Venables and Ripley describe a solution working under S+ (in MASS, 3. ed., p. 263) which requires the D system function and deriv3. This solution does not seem to be portable to R or at least I do not realise how. Is there anyone who a) knows how one could make the MASS-method run under

I am wondering if anybody here have a simple example in R for Naive Bayes. For example, I can do k-means clustering on the "iris" data - data(iris) cl <- kmeans(iris[,1:4], 3) cl$cluster cbind(1:150,iris$Species) =========== But how to do Naive Bayes classification in the same "iris" data? Many thanks! -- View this message in context: