`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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Performance of regresion tree rpart

I am running a regression tree using rpart and I would like to understand how well it is performing. I know that rpart has cross validation built in, so I should not divide the dataset before of the ...
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Rpart maximum depth

Right now I am using the Rpart library to classify text sentiment, but a problem that I have run into is that the maximum depth of the tree is 30. As a result when I use more than 400 features (or in ...
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Why is $\Delta r$ equal to 0?

From the Rpart documentation it gives an example as to why $\Delta r$, the change in risk is a bad indicator to use to determine whether the tree should be split, here is the paragraph: My question ...
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When using loss matrix in rpart in R, xerror does not start at 1 [closed]

I am trying to use a loss matrix in rpart penalizing false positives 10 times as much as false negatives, but when I fit my data and then use printcp, my xerror values start at 10 and not 1. I am ...
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74 views

Decision Tree Interpretation (Classification using rpart)

When using rpart to create classification tree, the values for the relative importance of each predictor show up along these lines: ...
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55 views

Why does ctree(partykit) perform worse than rpart for a large dataset?

I am trying to solve the same classification problem with the R packages rpart and partykit. I would have expected better ...
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177 views

How does the complexity parameter correspond to the number of splits in cross validation in rpart?

library(rpart) tree = rpart(Kyphosis ~ ., data=kyphosis, control=rpart.control(minsplit = 1, cp = 0, xval=10)) plotcp(tree, minline = FALSE, upper=c("splits")) ...
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64 views

Lack of fit for decision trees

I am using the cp argument in the rpart function in R. I would like to understand exactly how lack-of-fit is calculated for decision trees. Please provide a simple example if possible. Thanks. ...
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How exactly is the cost-complexity parameter in rpart selected?

I read the description of rpart and some other sources about classification trees. Still, I'm not sure if I completely understood how rpart selects these parameters. I would appreciate it if someone ...
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25 views

Is minimised cross-validated error equivalent to maximised cross-validated accuracy?

I'm running a classification tree using the function rpart. Usually, I choose the tree that minimises the cross-validation error. I was wondering, since error-rate is normally defined as 1-Accuracy. ...
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Why is there a difference between using the package rpart and using the method “rpart” in the caret package?

When I'm running rpart on my train set, it suggests a different cp than rpart does, even though I'm using the same seed. The two formulas I am using are: Using the Caret package fitControl <- ...
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How to find the optimal cp value in rpart doing cross validation manually?

I'd like to test two classifiers at the same time, that is logistic regression and classification trees. To find a classification threshold, which for example maximises F1-score, I split my data set ...
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Is using Rpart with unbalanced data a good idea?

I have a rather unbalanced data set and want to use rpart to build a classification tree. After building the full tree, I prune it back using the 1-SE rule. On average, only 1-2 splits are suggested. ...
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27 views

Consistent way of calculating average tree size using repeated cross validation?

I'd like to examine the behaviour of my classifier concerning the tree size. That is, I'm using rpart to build and prune the tree. For the rest of my analysis I'm running repeated 10 Fold Cross ...
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37 views

How can I rank the coefficient's contribution/importance for logistic regression?

My situation is the following; I'm running a classification tree (with the function rpart in R) and a logistic regression on a data set using 10 Fold cross-validation. Since I'm estimating the model ...
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38 views

Root node error in rpart

Can anyone provide the equation for "root node error" in rpart? I have a training data sample with 120 rows. Half of the training sample is classified as ...
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136 views

How can I forecast a time series using Cart models?

I'm using the rpart library to try forecasting the electricity consumption from Australia (example from the book Introductory Time Series with R): ...
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97 views

How cross validation error increases in CART while relative error decreases?

I am using a simulation dataset with rpart as follow: ...
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24 views

Randomize and give priority

I built one interesting model in R. This model is based on rpart library. The way I use this library is following (it may be ...
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76 views

Pruning an rpart tree

I've never played with the rpart package so I'm trying to get myself oriented. I followed the example from the docs and then tried to replicate it myself with my ...
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39 views

grow a regression tree one split at a time

I'm growing a regression tree with the rpart function in R (package of the same name). I would like to be able to choose myself the number of nodes (not the depth ...
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What does it mean when Root node error is greater than 1?

Using rpart, I see the error metrics with printcp(tree). I understand these metrics according to this answer. What does it mean when Root node error is greater than ...
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pruning : why if T1 and T2 (2 subtrees) with the same risk imply that one must be a subree of the other

I don't understand the following assertion from "An Introduction to Recursive Partitioning" page 13. If T1 and T2 are sub trees of T with Rα(T1) = Rα(T2), then either T1 is a sub tree of T2 or ...
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194 views

Plotcp in rpart package

I am trying to create a decision tree using the rpart package in R. To arrive at the optimal depth for the tree, I am using the ...
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1k views

Decision Trees rpart R categorical and continuous variables

I run some decision trees in rpart with 10 continuous variables and 3 categorical variables (with 1 or 0 options), the result of the tree was that none of the 3 ...
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Drawing tangible insights from rpart model splits

I'm working on a data set for an engineering problem which involves the use of pumps. My main objective is figuring out how to reduce the number of pumps used where the maximum number of pumps is 4 ...
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Why is CART in R not using my factor variables?

I have data which looks like ...
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113 views

CART with Ordinal Response Variable using rpartScore Stuck

I'm trying to fit a decision tree over some data which has ~40K rows and ~200 features. The response variable, y, is ordinal and takes values ...
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232 views

Setting different depth in rpart, but didn't actually change

I am tuning my decision tree with different depths. I wanted to see my results for trees with depth from 6 to 12. Instead of creating a loop, I am manually changing the parameter. My problem is, ...
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834 views

rpart classification: why is my predict() output not adhering to type=“class”?

I have a dataframe, 'datas', with 200 observations and a series of columns (some numeric, dummy, etc) and a binary class variable to be predicted that is called "bad_econ." I would like to get the ...
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161 views

Difference between weights and prior in rpart and how to use them

I have a question about the "weights" and "prior" in R's rpart function. This question has been asked before here, but the answer doesn't quite make sense. Currently I have very unbalanced data ...
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93 views

classification tree with R part

I am trying to grow a classification tree with a few continuous explanatory variables and a few factor variable. It seems the Rpart alogrithm is ignoring the factor variables. The differences are ...
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239 views

How to predict new data with a classification tree in R?

I have built a classification tree for factor variables with the rpart package and now I want to predict unseen data with it. How can I get a sense of whether the model is good at predicting unseen ...
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117 views

How do Conditional Inference Trees do binary classification?

I am trying to learn about Conditional Inference Trees, and have been doing some very simple comparisons of the ctree() and rpart() functions in R. I have looked at the documentation for ctree(), but ...
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415 views

Predictions for rpart model require more variables than shown in the classification tree

Using rpart from the caret package, when plotting the final model I get a classification tree that seems fairly simple (6 variables shown in tree). However, when I request the final variables from ...
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What is the equivalent of the complexity parameter ( rpart in R ) in python for regression trees (sklearn)?

The complexity parameter decides when to stop splitting. what is its equivalent in python. As decreasing the cp tends to increase the accuracy in the prediction, so is there a similar parameter in ...
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117 views

R: plot(tree) how to interpret the height?

Is there any signification in the height of the split in a tree when it's plotted with R ? Example: library(rpart) plot(tree) How can we interpret the fact than ...
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134 views

How to interpret concretely the misclassification error?

I'm reading about Cart classification with rpart on R, and after all we should compute the misclassification error, given that y is the column that stocks classes, and x is the variable columns and ...
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276 views

“Complete” classification using decision tree

While exploring the examples from "Practical Data Science with R", I am using a decision tree to classify the spambase dataset. It works fine, but I am trying to "abuse" the model in order to have ...
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Is the rpart CART algorithm deterministic? Why are the plots for the CP different?

I'm fitting regression and classification trees. I thought that the algorithm to fit the tree led to the same result each time. However, when I run the line below ...
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53 views

Non-recursive regression tree

I would like to construct a regression tree such that each factor appears at most once on each branch. When I use rpart in R, it commonly results in a tree where ...
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247 views

Regression trees to model rates

I am performing a predictive modeling application where I have to predict claims. If I had used classical GLMs, I would have used a poisson glm using log exposure as offset, assuming therefore ...
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317 views

Extract training data predictions from rpart

I'm wondering if there is any method to extract the class assignment of each sample in an rpart model from the training data? E.g. in R using random forest to get the predicted class of each sample ...
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212 views

How to Force Split Decision Tree in R

Is there any ways that a user can force split the decision tree in R ? I just think when building decision tree, we may need expert judgement in order to obtain the final model. As far as I know, ...
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688 views

Understanding RPART model results

I have operational fault data and maintenance data. The operational fault data was used to determine if the maintenance improved the fault indicator (true/false). The maintenance data was used to ...
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99 views

The Strength of the Decision in Decision Tree

I learn how to use decision tree in R library(rpart) fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis) asRules(fit) return, ...
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2k views

Interpreting rpart output for decision trees?

How do I go about selecting the ideal location to use for pruning the tree here? Or maybe someone can explain to me in simple language what this output means. I see that rel_error is constantly ...
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452 views

Rpart using Caret changes names of Factors

If I have a factor e.g. sexe with two levels MALE and FEMELLE let's say, using rpart alone I get splits that say for example Sexe = Male and then a yes no split. However using rpart with caret I get a ...
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691 views

How to evaluate the goodness of fit for survial functions

I am a newcomer to survival analysis, although I have some knowledge in classification and regression. For regression, we have MSE and R square statistics. But how we can say that survival model A ...
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191 views

Propensity score matching: using alternative methods to create a distance measure

I would like to use a greedy nearest neighbour method to do propensity score matching. Though I've little experience here, it seems that the distance measure used is generally a propensity score ...