`rpart` is an R package that provides a number of routines related to regression trees and recursive partitioning algorithms. This package is frequently used for classification problems.
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Loss Matrix Equivalent with Neural Networks and random Forest
I'm doing classification (0,1) on a dataset for which different types of errors should be weighted differently. IE, false positives would be weighted 10 x more than false negatives.
In decision ...
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Various models not improving basic rpart result
I have a data set with 10,000 or so samples in it and 100 or so features. I've created a training set and test set and am trying to predict a numeric value. I've used rpart to determine the most ...
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how does rpart handle missing values in predictors?
From the ?rpart documentation -
na.action : the default action deletes all observations for which y is missing, but keeps those in which one or more predictors ...
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Understanding factors returned by RPart classification
This is a very basic question on using R for classification. I'm trying to use rpart for classification task and would like to have a class label as a result, i.e. I use type="class" in predict method
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Choosing complexity parameter in CART
In the rpart() routine to create CART models, you specify the complexity parameter to which you want to prune your tree. I have seen two different recommendations for choosing the complexity ...
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Is it possible to have xerror increased in a tree using rpart?
I am new to R and rpart package. When I plot the tree using rpart:
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CART with rpart and a 12 level factor
I have a 12-level factor variable (month) in my dataset and I wanted to fit a CART tree with rpart(). Would you split the 12-level factor variable into 12 dummy variables?
If I fit the model with one ...
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488 views
rpart and the printcp function
I don't really understand how the columns "xerror" and "rel error" are calculated.
I found out that the printcp() function "gives cross-validation estimates of misclassication error (xerror), ...
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CART (rpart) balanced vs. unbalanced dataset
I am fitting a tree (CART) to the olives-dataset. The training data has 436 observations (test data: 136). I have 3 responses (the 'Region' variable) which splits the training data into 116 / 74 / 246 ...
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decision tree using user defined split function in rpart: No splits returned when tree is run
I've written a user defined splitting function to use with rpart, its returning a 'vector of goodness', but the tree that is returned never has any splits, just one node.
using the anova method on ...
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party vs. rpart vs. ??? for partitioning trees in R
It's been a while since I looked at partitioning trees. Last time I did this sort of thing, I like party in R (created by Hothorn). The idea of conditional inference via sampling makes sense to me. ...
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Difference in implementation of binary splits in decision trees
I am curious about the practical implementation of a binary split in a decision tree - as it relates to levels of a categorical predictor $X{j}$.
Specifically, I often will utilize some sort of ...
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402 views
Overcoming memory constraints in rpart?
I have 4.4 million observations, 160 binary features, and a binary response. Using rpart on Windows (64-bit, with the 64-bit R v2.13.0 build), I run out of memory on a machine with 64GB RAM. My memory ...
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Recursive partitioning using median (instead of mean)
As I am only familiar with the basics regarding decision trees I would like to ask, with the risk of stating silly question: Is it possible to perform recursive partitioning with the group median as ...
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412 views
How to choose the number of splits in rpart()?
I have used rpart.control for minsplit=2, and got the following results from rpart() ...
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2answers
531 views
Recursive partitioning using rpart() method in R
I am new to R and using rpart for building a regression tree for my data.I wanted to use all the input variables for building the tree, but the rpart method using ...
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1answer
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Organizing a classification tree (in rpart) into a set of rules?
Is there a way that once a complex classification tree is constructed using rpart (in R), to organize the decision rules produced for each class? So instead of getting one huge tree, we get a set of ...
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How to produce a CI for a value predicted in CART?
My goal is to create CI for the CART prediction of new_x
Consider the following code:
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3answers
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What is Deviance? (specifically in CART/rpart)
What is "Deviance," how is it calculated, and what are its uses in different fields in statistics?
In particular, I'm personally interested in its uses in CART (and its implementation in rpart in R). ...
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How to measure/rank “variable importance” when using CART? (specifically using {rpart} from R)
When building a CART model (specifically classification tree) using rpart (in R), it is often interesting to know what is the importance of the various variables introduced to the model.
Thus, my ...
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1answer
390 views
R-square from rpart model
How can I extract the R-square from a fit rpart model?
rsq.rpart(fit)
plots the two graphs, but I simply want to extract the R-square value for the full tree. ...
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1answer
332 views
Estimate the “meaningful” predictors for a value in a CART model (rpart)
When building a CART model (specifically classification tree) using rpart (in R), it is sometimes obvious that there are variables (X's) that are meaningful for predicting some of the outcome (y) ...
