similar to: [Help! feature selection]

Displaying 20 results from an estimated 7000 matches similar to: "[Help! feature selection]"

2004 Apr 18
2
outliers using Random Forest
Hello, Does anybody know if the outscale option of randomForest yields the standarized version of the outlier measure for each case? or the results are only the raw values. Also I have notice that this measure presents very high variability. I mean if I repeat the experiment I am getting very different values for this measure and it is hard to flag the outliers. This does not happen with two other
2003 Apr 22
7
Subject: Eliminate repeated components from a vector X-Mailer: VM 7.00 under 21.4 (patch 6) "Common Lisp" XEmacs Lucid Reply-To: fjmolina at lbl.gov FCC: /home/f/.xemacs/mail/sent Does anyone know how I can eliminate repeated elements from a vector?
2004 Mar 13
4
nnet classification accuracy vs. other models
I was wandering if anybody ever tried to compare the classification accuracy of nnet to other (rpart, tree, bagging) models. From what I know, there is no reason to expect a significant difference in classification accuracy between these models, yet in my particular case I get about 10% error rate for tree, rpart and bagging model and 80% error rate for nnet, applied to the same data. Thanks.
2004 Oct 25
1
Feature selection
Hello, I want to do feature selection for classification purpose (using lda). Can someone point me to any R package or S-plus package for this? Something like SFS or SFFS method would be useful for me. Thanks. Raj [[alternative HTML version deleted]]
2005 Jun 09
1
Help with SOM membership
Hi all, I originally posted this to the bioconductor group, but maybe it's better suited to the r-help... I'm using som() to partition samples of gene expression data into clusters. The point is to classify control vs. experimental cases (sample clustering). The original matrix was 22283 x 8. The 8 samples have 4 controls and 4 experimentals. I transposed the matrix so that its dim
2009 Dec 01
2
problem with RWeka Weka_control RandomForest
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
2009 Apr 27
1
MDL - Fayyad & Irani Discretization
Hey, I'm looking for a function which provides a supervised Fayyad & Irani discretization (MDL). I've already found RWeka which has such a function. But is there anything else? I'd prefer to use sth lighter in my own package. Regards, Peter
2011 Jan 20
1
randomForest: too many elements specified?
I getting "Error in matrix(0, n, n) : too many elements specified" while building randomForest model, which looks like memory allocation error. Software versions are: randomForest 4.5-25, R version 2.7.1 Dataset is big (~90K rows, ~200 columns), but this is on a big machine ( ~120G RAM) and I call randomForest like this: randomForest(x,y) i.e. in supervised mode and not requesting
2008 Feb 03
2
use classificators learned in R in "real-life", e.g. C
Hi there, I am interested in using R for machine learning (supervised classification). Currently, I have been investigating especially the rpart, tree, and randomForest package, and have achieved first results. are there any experiences, how the learned classificators could be used in e.g. C ? in other words, I want to "transfer" the learned predictor from R to C-code. for e.g. rpart,
2010 May 26
3
cluster analysis and supervised classification: an alternative to knn1?
Hi, I have a 1.000 observations with 10 attributes (of different types: numeric, dicotomic, categorical ecc..) and a measure M. I need to cluster these observations in order to assign a new observation (with the same 10 attributes but not the measure) to a cluster. I want to calculate for the new observation a measure as the average of the meausures M of the observations in the cluster
2011 May 12
2
Can ROC be used as a metric for optimal model selection for randomForest?
Dear all, I am using the "caret" Package for predictors selection with a randomForest model. The following is the train function: rfFit<- train(x=trainRatios, y=trainClass, method="rf", importance = TRUE, do.trace = 100, keep.inbag = TRUE, tuneGrid = grid, trControl=bootControl, scale = TRUE, metric = "ROC") I wanted to use ROC as the metric for variable
2008 Jun 15
1
randomForest, 'No forest component...' error while calling Predict()
Dear R-users, While making a prediction using the randomForest function (package randomForest) I'm getting the following error message: "Error in predict.randomForest(model, newdata = CV) : No forest component in the object" Here's my complete code. For reproducing this task, please find my 2 data sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).
2023 Mar 19
1
ver el código de randomForest
Buenos días: Otra opción es escribir directamente el nombre de la función en la consola de R: > randomForest function (x, ...) UseMethod("randomForest") En este caso, la función randomForest() llama a UseMethod() para seleccionar el método adecuado. Podemos ver los métodos para randomForest con la función methods(): > methods(randomForest) [1] randomForest.default*
2006 Apr 18
2
installation of package "randomForest" failed
Hello I'd like to try out some functions in the package randomForest. Therefore, I did install this package. However, it is not possible to load the library, although I have R-Version 2.1.1 (i.e. later than 2.0.0). The commands I used and the Answers/Error from R is as follows: > install.packages("C://Programme//R//rw2011//library//randomForest_4.5-16.zip",
2010 May 10
2
Installing randomForest on Ubuntu Errors
Hello, I've tried to install randomForest on a Ubuntu 8.04 Hardy Heron system. I've repeatedly rec'd the error: > install.packages("randomForest", dependencies = TRUE) ERROR: compiliation failed for package 'randomForest' ** Removing '/home/admuser/R/i486-pc-linux-gnu-library/2.6/randomForest' The downloaded packages are in
2007 Apr 29
1
randomForest gives different results for formula call v. x, y methods. Why?
Just out of curiosity, I took the default "iris" example in the RF helpfile... but seeing the admonition against using the formula interface for large data sets, I wanted to play around a bit to see how the various options affected the output. Found something interesting I couldn't find documentation for... Just like the example... > set.seed(12) # to be sure I have
2013 Feb 14
1
party::cforest - predict?
What is the function call interface for predict in the package party for cforest? I am looking at the documentation (the vignette) and ?cforest and from the examples I see that one can call the function predict on a cforest classifier. The method predict seems to be a method of the class RandomForest objects of which are returned by cforest. --------------------------- > cf.model =
2012 Apr 10
1
Help predicting random forest-like data
Hi, I have been using some code for multivariate random forests. The output from this code is a list object with all the same values as from randomForest, but the model object is, of course, not of the class randomForest. So, I was hoping to modify the code for predict.randomForest to work for predicting the multivariate model to new data. This is my first attempt at modifying code from a
2005 Jan 06
1
different result from the same errorest() in library( ipred)
Dear all, Does anybody can explain this: different results got when all the same parameters are used in the errorest() in library ipred, as the following? errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv", est.para=control.errorest(k=3), mtry=2)$err [1] 0.03333333 > errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv",
2008 Jul 22
2
randomForest Tutorial
I am new to R and I'd like to use the randomForest package for my thesis (identifying important variables for more detailed analysis with other software). I have found extremely well written and helpful information on the usage of R. Unfortunately it seems to be very difficult to find similarly detailed tutorials for randomForest, and I just can't get it work with the information on