Stands for 'Classification And Regression Trees'. CART is a technique for developing a tree model (T) to predict categories (C) and/or continuous values (R) by recursive partitioning. It does not make restrictive parametric assumptions. CART is a popular data mining technique.

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feature selection for longitudinal data

I have a longitudinal data which looks like this. Number of time points are different for each ID. Y is the binary response variable (take values 0 & 1) and X1-X20 are either continuous or ...
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26 views

Obtaining easy-to-interpret decision tree

I am trying to create a decision-tree out of a number of attributes, where there are only two final classes and the classes are highly unbalanced (Class 1: 95.5%; Class 2: 4.5%). The idea is to ...
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41 views

Post hoc selection of important features in random forest?

I want to guarantee a parsimonious random forest (few features used). What are methods to do this? It was suggested to me to get the feature importance after the model was created, and then create a ...
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12 views

Considering non-i.i.d. covariates in random forests

Random forests are theoretically funded on the assumption that the data are i.i.d. realizations from a multivariate random vector $(X_1, \ldots, X_p, Y)$. Does it make sense to use random forests (for ...
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29 views

categorical feature ranking

I would like to rank categorical features by the order or importance in a classification/regression setting. Input There are two features, which are survey questions: "how is your mood?": four ...
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14 views

Minimum population for decision trees and association rules

Hi I'm quite new to this and I'm playing around with R and Microsoft's SSAS. Does anyone have a rule of thumb how big a data set has to be for association rules and decision trees to be statistically ...
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27 views

A question on no. of training examples and decision trees

I have a set of around 200,000 training instances. Each training instance consists of an attribute called $duration$, a discrete integer type and a time series of floating-point values, in form of a ...
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13 views

On population variable importance

Consider we run a random forest on $n$ independent realizations of a random vector $(X_1,X_2,X_3,Y)$ assuming $Y$ is a numerical response variable. Let $f$ be the best theoretical classifier defined ...
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9 views

How does RPART pick a splitter when there are at least 2 splits having maximal information gain?

I'm not sure the best way to explain this, so let me give an example that motivates my question. I have tried reading the RPART manual, documentation, and its code, but I have not been able to resolve ...
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5 views

Design a feature with time and presence information

Context: I am working on a decision tree classifier, trying to classify businesses as to whether they are likely to have an event occur (default) in the next 90 days. One input I get is whether, and ...
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4 views

Is there any way to manipulate the titles of a ctree plot? [migrated]

Is there any way to change the title sizes of a ctree plot? Use the following variables to quickly set up a ctree plot ...
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16 views

Evaluating mean-squared error

Hello I am running a Regression Tree experiment. I am new to Regression Trees, and I am using Mean Squared error to test my tree. I am confused because I am getting a large Mean Squared Error but I ...
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70 views

Why should all Cross-Validation results be higher than the result on the test dataset?

Sorry, I'm not an expert and my question could be fundamentally wrong. I've read this interesting question because I also was wondering whether to train the model again after cross-validation. Now, ...
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35 views

Is there such thing as correlation trees? Clustering rows of X based on correlation between A and B

I have been searching for several days for a method that fits this description, though cannot find one. I'm pretty sure it must exist. The problem (short version): I'd like to run something like a ...
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1answer
19 views

Decision Trees on training data

Wouldn't any decision tree trained on a training data set have no errors in classification? In other words, wouldn't every data point be classified correctly in the training data set? How would this ...
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22 views

Misclassification Rate

The misclassfication rate in decision trees is defined as $$1-\max_{j} p_{j}$$ Suppose we want to classify people into republicans and democrats. In the training data, $\hat{p}_1 = 0.7$ (probability ...
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7 views

Comparing and contrasting classification trees over the years

At work I have decided to use a classification tree to do some analysis on the people at the bottom decile for hourly pay. Mainly for inferential reasons. My datasets are broken up in years, I have ...
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29 views

In CHAID, shouldn't we merge categories when p<alpha rather than p>alpha?

In CHAID, the categories are merged when P>alpha in the first step. BUT Since CHAID uses Chi-square statisitic, if p-value < alpha, we reject the null ( Independence), hence, meaning the ...
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1answer
23 views

What methods can be used to transform data?

I am solving a binary classification problem with 4 predictor variables. The variables didn't seem to be linearly separable. I have used Neural Networks and Kernel SVM which work and give desired ...
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18 views

Down-sampling with building models (specifically random forests)

I was wondering if anyone had ever used down-sampling to build random forests with data that has unbalanced classes. Basically down-sampling samples (with replacement) x*min from the population where ...
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21 views

building a decision tree after association rules

I built association rules using R Arules package. Then I filtered those rules and kept rules that have a specific variable on RHS. That variable is my Y variable. In entire dataset, my variable occurs ...
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12 views

Improving sentence segmentation in NLTK

I have been looking into problem of sentence segmentation lately. I have been referring to NLTK's book for this purpose. I followed their procedure to segment sentences presented here: ...
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62 views

Why will a random forest not outperform a regression tree?

I have a training dataset with a binary response variable, 6 independent variables, and 21,000 observations. I've fit both an ordinary regression tree and a random forest (mtry = 2, ntree = 2000) and ...
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61 views

Decision tree in R

I am new to machine learning in R. This is my data set. ...
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2answers
91 views

How to prune a decision tree properly in R

I have a sample of 12,500 observations and 12 explanatory variables. I want to build a pruning decision tree, to do that I am using the rpart function and then the ...
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14 views

decision tree : categorical vs numeric explanatory variables [duplicate]

I found similar topic but it dealt with the question whether it's better to discretize numerical dependent variable or leave it as it is. I'd like to find out what are your experiences with training ...
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42 views

How to promote a regression tree over a GLM?

Does anybody have any suggestions about promoting the use of a regression tree over a GLM when the two models fit the data almost exactly the same? My team's current arguments are a) a tree is ...
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16 views

Heuristic for bagging in random forests

Is there a good heuristic for what fraction of the data to use when training a random forest with bagging? I imagine this fraction should depend on the number of trees in the forest, like 2/3 ...
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38 views

How do i estimate the Weights of the predictions assigned to each of the tree in GBM using R? How does GBM split nodes?

I ran a GBM model in R with loss function as bernoulli and n.trees=1000. I want to see the weights assigned to the predictions coming from 1000 trees. Is there any command in R that does that? How ...
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1answer
22 views

multiple predictors in decision tree model

I am using like 10 predictors in my decision tree, but the rpart function uses only like 8 of them. does it mean that rest 2 are not needed or they become redundant? like i have age and child coded ...
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1answer
36 views

difference between rel error and xerror regression trees (rpart)

Wondering what the difference is between rel error (relative error) and xerror (apparent error) in regression trees? I am using the rpart package and the output returns these metrics ...
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15 views

CART & stationarity assumptions

I have a question regarding if there are any stationarity requirements for a regression tree. If I were to regress 2 share prices on each other (normal linear regression), it would be classified as ...
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89 views

Prediction problem: Do I have to sample the data set so that the outcomes are balanced?

I want to predict whether a loan is default or fully paid, with about 20 features and 10,000 historical observations. Among the data over 85% are fully paid, 15% are default, I want to try ...
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30 views

regression trees + scale of output variable

I am developing a regression tree model I have an output variable with a very large standard deviation, I am wondering if I need to scale/normalize this output variable as metrics such as RMSE and R^2 ...
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27 views

Choice of test set for classification

I have 50 measurements of 10 descriptors and 1 binary output variable. I want to use a classification procedure to be able to predict the output, so I split the data into a training and a test set ...
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15 views

How to determine the conversion path?

A user interacts with X number of features before they go on to convert. How do I determine which features, and in what order, is the most common path to conversion? For example, if I was doing ...
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44 views

Classification/Regression Tree with nonngeative response

I try to model the duration until a unit is inspected by a large number of possible explanatory variables. The duration is non-negative and the explanatory variables are factors and numerical ...
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1answer
66 views

encrypted data on CART, ID3

Some data are confidential such as patient data. Therefore sometimes companies does not want to give original patient data instead they first encrypt it(for instance with SHA1) and then give. If we ...
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2answers
65 views

Combine decision trees from GBM to reduce output

I am curious if any research has been conducted to efficiently combine trees resulting from a gradient boosting process. I routinely run a process that generates 20 or 30 thousand trees in R. I then ...
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1answer
38 views

SPSS CHAID/CRT Query

Hello I am using SPSS trees - CART functionality for classification. I have a number of classifying variables such as Business_Size, Location, Previous_Record etc. I want to know how do I set ...
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2answers
103 views

How can I cluster data in a grid-like fashion and heat map the averages in R?

I have a data frame of 3 columns. The first one is the response variable the second and the third ones are some criteria. You can create your own example similar to mine, using this piece of code with ...
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1answer
151 views

Regression Trees / Boosted Regression Trees for Tweedie Distribution in R

I am currently working at work on a project that attempts to predict an environmental change variable. I am personally not a huge fan of the project, but I still want to do the best job possible. ...
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1answer
85 views

fit and cross-validate categorical sample data formed from observations

I am working with the following sample dataset: ...
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79 views

Benefits of CART over ID3 algorithm

When building decision trees over a dataset that generates nodes with bad purity, is there any benefit of using the CART algorithm over the iterative dichotomizer 3 (ID3) algorithm?
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32 views

Unsupervised Decision Trees

As far as I know, decision trees are always used with labeled data to learn rules which differentiate between different labels. But is there anything called unsupervised decision tree? Say I have an ...
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1answer
44 views

OLS vs regression / classfication tree

I am using regression tree to find the factors effecting food insecurity. I want to know how regression tree is better than OLS in terms of heterogeneity. Can anybody help?
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88 views

How do decision tree learning algorithms deal with missing values (under the hood)

What are the methods that decision tree learning algorithms use to deal with missing values. Do they simply full the slot in using a value called missing? Thanks.
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131 views

Gini decrease and Gini impurity of children nodes

I'm working on the Gini feature importance measure for random forest. Therefore, I need to calculate the Gini decrease in node impurity. Here is the way I do so, which leads to a conflict with the ...
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2answers
46 views

Software for assisted decision tree construction

I have a large dataset based on several thousand surveys consisting of hundreds of questions each. I would like to form a classification tree semi-automatically as follows. Each node of the tree can ...
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19 views

If my feature of interest has many values should I pre-process them into groups?

I'm quite new to machine learning, pattern recognition, statistics, etc. but I'm trying to wrap around how a machine learning system would interpret data that is something like this: row0-> ...