R Rpart taking too much time on data set I am trying to build a CART model using  rpart on a data set with around 7k rows and 456 columns 

cmodel2=rpart(DV ~ .,data=teltrain2,method="class")


This has never returned a result yet. I am using a 16GB ram machine with R 3.2.3. Every time on running this, R execution goes on and on, I have to kill the R GUI process to finally stop it.

Description of the columns


id --> unique id (which I have removed from the training set)  
Location --> number   
col3 to col400 --> these have been hot encoded by me and include just 0 or 1 as values
col400-col456 --> these are features which have numeric values like 23,144 etc, all less than 1000 
DV --> dependent variable is categorical with more than 2 values.


I have tried running rpart with a smaller set of 1000 rows and 456 cols, still no luck. I don't want to do any kind of feature elimination on the first run as as of now I have no idea whether they affect the dependent variable or not.

I have tried mentioning minbucket=25,20,15,10 with no luck either.

My objective is to get the resulting model of rpart on the entire data set. What are my options?
Have you tried using randomForest instead of rpart? For example, let's assume you have two data.frames:

train_data and test_data

In my example, the last column is the class (and is a factor variable), and all other variables (all other columns) are numeric. For example, one training example might look like:

4 5 10 12 1 0

where 0 is the class label. You can do the following to run a random forest with 50 trees:

library(randomForest)
model_pl_1 &lt;- randomForest(train_data[,-ncol(train_data)], train_data[,ncol(train_data)], test_data[,-ncol(test_data)], test_data[,ncol(test_data)], ntree=50)