similar to: 10-Fold Cross Validation AND Random Forest

Displaying 20 results from an estimated 10000 matches similar to: "10-Fold Cross Validation AND Random Forest"

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)$err [1] 0.03333333 > errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv",
2006 Oct 08
0
Problem in getting 632plus error using randomForest by ipred!
Hello! I'm Taeho, a graduate student in South Korea. In order to get .632+ bootstrap error using random forest, I have tried to use 'ipred' package; more specifically the function 'errorest' has been used. Following the guidelines, I made a simple command line like below: error<-errorest(class ~ ., data=data, model=randomForest, estimator = "632plus")$err
2005 Mar 18
2
logistic model cross validation resolved
This post is NOT a question, but an answer. For readers please disregard all earlier posts by myself about this question. I'm posting for two reasons. First to say thanks, especially to Dimitris, for suggesting the use of errorest in the ipred library. Second, so that the solution to this problem is in the archives in case it gets asked again. If one wants to run a k-fold cross-validation
2009 Nov 02
1
modifying predict.nnet() to function with errorest()
Greetings, I am having trouble calculating artificial neural network misclassification errors using errorest() from the ipred package. I have had no problems estimating the values with randomForest() or svm(), but can't seem to get it to work with nnet(). I believe this is due to the output of the predict.nnet() function within cv.factor(). Below is a quick example of the problem I'm
2009 Apr 25
1
Overlapping parameters "k" in different functions in "ipred"
Dear List, I have a question regarding "ipred" package. Under 10-fold cv, for different knn ( = 1,3,...25), I am getting same misclassification errors: ############################################# library(ipred) data(iris) cv.k = 10 ## 10-fold cross-validation bwpredict.knn <- function(object, newdata) predict.ipredknn(object, newdata, type="class") for (i in
2005 Jan 06
1
leave-one-out cross validation for randomForest
Dear all, Can I get the leave-one-out cross validation error of randomForest in R? I only found tune(), which got the 10-fold cross validation error. Thanks for any information. Xin LIU This e-mail is from ArraDx Ltd The e-mail and any files transmitted with it are confidentia...{{dropped}}
2005 Jun 23
1
errorest
Hi, I am using errorest function from ipred package. I am hoping to perform "bootstrap 0.632+" and "bootstrap leave one out". According to the manual page for errorest, i use the following command: ce632[i]<-errorest(ytrain ~., data=mydata, model=lda, estimator=c("boot","632plus"), predict=mypredict.lda)$error It didn't work. I then tried the
2006 Feb 02
0
crossvalidation in svm regression in e1071 gives incorrect results (PR#8554)
Full_Name: Noel O'Boyle Version: 2.1.0 OS: Debian GNU/Linux Sarge Submission from: (NULL) (131.111.8.96) (1) Description of error The 10-fold CV option for the svm function in e1071 appears to give incorrect results for the rmse. The example code in (3) uses the example regression data in the svm documentation. The rmse for internal prediction is 0.24. It is expected the 10-fold CV rmse
2006 Feb 02
0
crossvalidation in svm regression in e1071 gives incorre ct results (PR#8554)
1. This is _not_ a bug in R itself. Please don't use R's bug reporting system for contributed packages. 2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take sqrt. 3. You really should use the `tot.MSE' component rather than the mean of the `MSE' component, but this is only a very small difference. So, instead of spread[i] <- mean(mysvm$MSE), you
2012 Mar 03
0
Strategies to deal with unbalanced classification data in randomForest
Hello all, I have become somewhat confused with options available for dealing with a highly unbalanced data set (10000 in one class, 50 in the other). As a summary I am unsure: a) if I am perform the two class weighting methods properly, b) if the data are too unbalanced and that this type of analysis is appropriate and c) if there is any interaction between the weighting for class imbalances
2004 Jan 09
3
ipred and lda
Dear all, can anybody help me with the program below? The function predict.lda seems to be defined but cannot be used by errortest. The R version is 1.7.1 Thanks in advance, Stefan ---------------- library("MASS"); library("ipred"); data(iris3); tr <- sample(1:50, 25); train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]); test <- rbind(iris3[-tr,,1],
2005 Jan 10
0
Stadard errors and boxplots with 632plus error estimator, "errorest"
Dear R-users, I'd like to estimate standard errors (for lda) and make a boxplot with the "632plus" and "boot" error estimators included in package ipred (method: errorest). The "boot" estimator returns only a standard deviation but not the whole error data. Thank you in advance, regards, Antoine
2011 Sep 13
1
class weights with Random Forest
Hi All, I am looking for a reference that explains how the randomForest function in the randomForest package uses the classwt parameter. Here: http://tolstoy.newcastle.edu.au/R/e4/help/08/05/12088.html Andy Liaw suggests not using classwt. And according to: http://r.789695.n4.nabble.com/R-help-with-RandomForest-classwt-option-td817149.html it has "not been implemented" as of 2007.
2010 Aug 06
1
Error on random forest variable importance estimates
Hello, I am using the R randomForest package to classify variable stars. I have a training set of 1755 stars described by (too) many variables. Some of these variables are highly correlated. I believe that I understand how randomForest works and how the variable importance are evaluated (through variable permutations). Here are my questions. 1) variable importance error? Is there any ways
2012 Oct 17
1
Random Forest for multiple categorical variables
Dear all, I have the following data set. V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 alpha beta 1 11 1 11 1 11 1 11 1 11 alpha beta1 2 12 2 12 2 12 2 12 2 12 alpha beta1 3 13 3 13 3 13 3 13 3 13 alpha beta1 4 14 4 14 4 14 4 14 4 14 alpha beta1 5 15 5 15 5 15 5 15 5
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
2009 Jun 19
0
FW: Can I estimate strength and correlation of Random Forest in R package " randomForest"?
Didn't realize the message was cc'ed to R-help. Here's my reply... ________________________________ From: Liaw, Andy Sent: Thursday, June 18, 2009 11:35 AM To: 'Li GUO' Subject: RE: Can I estimate strength and correlation of Random Forest in R package " randomForest"? The strength and correlation among trees in a random forest are based on the predictions of
2013 Jan 15
1
Random Forest Error for Factor to Character column
Hi, Can someone please offer me some guidance? I imported some data. One of the columns called "JOBTITLE" when imported was imported as a factor column with 416 levels. I subset the data in such a way that only 4 levels have data in "JOBTITLE" and tried running randomForest but it complained about "JOBTITLE" having more than 32 categories. I know that is the limit
2011 Oct 10
1
pmml for random forest & rules
Hi, I am having some trouble using R 2.13.1 for generating a pmml object of of class "c('randomForest.formula', 'randomForest')" I see that these methods are available: > methods(pmml) [1] pmml.coxph* pmml.hclust* pmml.itemsets* pmml.kmeans* pmml.ksvm* pmml.lm* pmml.multinom* pmml.nnet* pmml.rpart* [10] pmml.rsf* pmml.rules* pmml.survreg*
2007 Jul 12
1
Package for .632 (and .632+) bootstrap and the cross-validation of ROC Parameters
Hi users, I need to calculate .632 (and .632+) bootstrap and the cross-validation of area under curve (AUC) to compare my models. Is there any package for the same. I know about 'ipred' and using it i can calculate misclassification errors. Please help. It's urgent. -- View this message in context: