Questions tagged [rpart]

`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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What is the meaning of dev in rpart()\$frame\$dev

In the R rpart library there is a function rpart which returns a fitted decision tree. Say ...
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Honest causal decision trees

Is there anyone who is familiar with honest causal decision trees? What is the purpose of the same and how can I implement it in my code in R? I am looking into the predictors (cognitive, non-...
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Existing library to build decision tree on sparse data

My apologies if this is too much of a repeat of another question I asked, but the question is important, and I am trying to learn from my experience of asking this question to ask it the right way. In ...
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How are surrogate splits used by rpart for variable importance when there are no missing data?

I have read the rpart vignette/ longintro. Its not clear how variable importance is measured especially this line: An overall measure of variable importance is the sum of the goodness of split ...
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Interpretation of mean absolute error in rpart

I ran rpart on my dataset (3000x9) to predict adolescent GPA (continuous), made predictions on test data, and found the mean absolute error to be in the range of 0.45-0.48. Does this mean that the ...
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Parameter Optimization in RF and rpart

I am using rpart and random forest in R to predict GPA (regression tree). On what basis do I decide the value of cp, minsplit, and minbucket? And on what basis do I decide the values of mtry and ntree ...
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Exploratory analysis : Regression trees without splitting train-test data

I am analyzing a small dataset of 76 observations and I want to explore how 9 environmental predictors explain my response variable. For this I have decided to use regression trees because I am ...
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Using a single regression tree for exploratory analysis [duplicate]

Currently I am working with small dataset, 76 samples where I am interested to model how one measure that I have (diversity) is shaped based on 9 environmental variables. We observe multicollinearity ...
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Estimate the optimal complexity parameter in CART by using grid search

In rpart I estimated the complexity parameter to which it is convenient to prune the tree. I used the grid search and the functions of another package, ...
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prediction error measures for Poisson regression in rpart

My question is about an alternative prediction error measure besides the default deviance-based error that is implemented but not documented. The vignette (section 8.1) states that: Prediction error: ...
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Issue poisson method rpart

I tried to use the rpart package to deal with observations count of crop pests, but I realized that there was overfitting with poisson method. In the vignette of the package, it said that it is ...
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Decision Tree model using Rpart

I am trying to create a node-link diagram (decision tree) by using parsnip and tidymodels. What I am performing is building a ...
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Splitting algorithm for survival tree in rpart package

I'm modeling Survival Tree using the package $\fbox{rpart}$ in R. Do you know which splitting criterion is used in this package to build the tree ? I tried to look at its document but it seems that ...
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Plot a tree from RandomForest object

I trained a randomforest using the RandomForest package on R. I am interested in how the most useful variables are split into the classes, So i would like to visualize a tree that is somehow an ...
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Interpreting Rpart.plot() for a Regression tree

I generally understand the CART algorithm but the rpart.plot values are confusing me a bit. Below is a picture of my plot. What exactly is the root node value ...
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Why does rpart not produce a perfect prediction when forced to?

I am trying to understand rpart all the details in rpart package. I am aware of the complexity parameter cp, which prevents a split if the improvement is less than <...
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Different results using randomForest::randomForest with one tree vs rpart

I am wondering what randomForest package handles differently for each individual tree. If I build a random forest with a single tree, no resampling, and allow the usage of all features in a dataset, I ...
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glmertree to fit logistic regression with two-column y

With both glm and glmer, if I wanted to fit a proportion, I could do it as either: ...
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How to interpret output of rpart model with survival object as response?

What do the numbers in terminal nodes represent? And how to interpret the predicted value for new observation obtained from function predict() ?
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GLMERTREE with reponse in [0, 1] and multilevel design

I have multilevel data (with nested random effects: (1 | cluster-of-cluster/cluster) in lme4 syntax) where the response is a continuous variable between $[0, 1]$ (i....
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Why is the error rate from bagging trees much higher than that from a single tree?

I'm running the classification method Bagging Tree (Bootstrap Aggregation) and compare the misclassification error rate with one from one single tree. We expect that the result from bagging tree is ...
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Regression trees with geepack package

I can’t run regression trees without geeglm. I have longitudinal data so rpart wouldn’t work. Is there a way to get regression trees with geeglm?
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How does decision tree decide which variable to use in next split?

The CART (or RPART) algorithm uses gini index to find a threshold value for a variable in each split. But how does it choose which variable will it use for splitting ?
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Does it make sense to use bayesian optimization for tuning of hyperparameters of decision tree model?

Does it make sense to use bayesian optimization for tuning of hyperparameters of decision tree model? I have not found any article or anything related to this, as BO is usually used for black-box ...
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R: rpart or random forest for datasets with multiple rows per subject [closed]

I have some fundamental understanding problem with rpart or train(method="rf") in R. My data is currently structured as follows: Around 100 subjects, each has 2048 rows (so around 204,800 rows) with ...
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When does OLS Regression outperform regression tree in term of out of sample prediction?

In my Master thesis i compare ols regression to regression tree to predict wages. I thought that i will get better prediction with the regression tree because it cathes more interactions. But now i ...
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Rpart and subsets

I am getting unexpected results from an rpart model, where the model selects two variables, one of which is a subset of the other. This in itself is not unexpected, but the seemingly odd thing is that ...
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Why do RMSE values increase on a smaller tree (RPART)

AIM: I want to understand why does RMSE increase on a smaller tree. CONTEXT: I am learning the rpart algorithm. I had some data,...
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Metrics in rpart decision trees

I am currently working with decision trees in R, I am using caret library. Source code of rpart can be found here: https://github.com/cran/rpart/blob/master/R/rpart.R I understand how decision trees ...
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classification tree with rpart()

I'm using the rpart() to build a classification tree using R. I have no experience in this topic... Anyway, I started with the full model, and then I used the varImp() from "caret" to drop some ...
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Performance of Conditional Inference regression Trees updating the influence function at each node

My goal is to compare the performance of $2$ models of trees using the Conditional Inference tree framework described in (ctree: Conditional Inference Trees), I am following the Partykit 2018. ...
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Test statistics used for a conditional inference regression tree?

Following the question asked previously about the interpretation of the Test Statistic used for Conditional Inference Trees (What is the test statistics used for a conditional inference regression ...
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Is rpart randomizing the variables it tries as splits?

I am fitting a decision with rpart on R. I have many variables and it seems to me that it would take a long time for rpart to try them all at every step, to determine what the next best split is. ...
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Are decision trees sensitive to log translations in feature space?

This question was partially answered on Are decision trees sensitive to translations in feature space?, but no references were provided for "Gini impurity and entropy measures are translation ...
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Interpretation of Rpart for Decision Trees

I recently used rpart for an R-decision tree, but am confused on how to read the results.... ...
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Decision Tree in R: rpart on categorical variables

Introduction: I would like to build a classifier which distinguishes between buyers and non-buyers based on user behavior. This data is highly imbalanced (0.009% for positive class), and I'm ...
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Classification in R - seperate category for uncertain classifications

I am constructing classification trees for the first time, so I'm quite new to this use of R. I have observations of behaviours and incoming data that has to be classified as one of these behaviours. ...
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discretization to create intervals for continuous variables

I am new to R and have basic stats understanding. Please excuse me if my questions are basic in nature. I am learning and having these questions and your answers would help me in updating my knowledge....
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Use decision tree to learn precise classification rules: need accuracy of 1

I'm trying to use a decision tree algorithm to learn how general ledger transactions (10 digit code) are classified into revenue, expense, G&A et cetera without actually scripting every ...
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How to interpret variable.importance for an rpart object

How can I interpret the values for the variable.importance in an rpart object? ...
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How can I use estimated probabilities of a class from rpart to identify the top N classes?

Using the rpart library, I'm trying to predict which class each observation belongs to. Here is a reproducible example explaining the steps I am taking: ...
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Disrepencies between Information Gain and Tree Growth

I was wondering if someone can explain to me why in my decision tree some of the variable with the highest importance (highest information - script shown below) do not appear into my tree at all, ...
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What does a decision tree with both GOOD outcome means?

I have a decision tree built in R using rpart() from rpart package. However, when following the nodes, we have one condition leading to both outcomes as GOOD. This is weird for me. What does that ...
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Handle missing value in continuous variable in Decision Tree without losing its meaning

I want to train a Decision Tree model with a dataset, of which some of the continuous variables contain missing values. I want to preserve the meaning of missing value while training, meaning that ...
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1 vote
1 answer
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Understanding rpart package - unexpected behavior when swaping two columns in explanatory varaibles table

I have a problem understanding the behavior of rpart function of R. Here is the r code part : ...
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Loss matrix to be included in decision tree? Rpart -R

For loss matrix, is it necessary to include it during the decision tree analysis ? What will be the impact if this is excluded from the analysis e.g loss matrix (0,1,1,0) in Rpart-R? Do we usually ...
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Continuous output instead of classification

I was trying to do some classification problem where target variable should be one of 4 classes. I tried rpart and ranger ...
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Classification tree cross val error increasing but test error decreasing

My data has 5000 points. I predict a categorical outcome using 10 numeric variables. I build a tree with 3500 points (in R using rpart) using cross validation and plotcp shows this below ... I have ...
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Use of regression-trees to determine probabilities for a binary variable

I have a binary variable (sold/not sold) and I have used the CART algorithm in R (rpart) to build a classification tree to predict if this product is getting sold or not. Now I would like to add a ...
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rpart, Cross Validation. [closed]

I have a question of using the rpart for the regression tree. I am wondering when i use plotcp, where would my validation data comes from? Currently, I have a ...
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