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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11 views

Regarding post pruning in decision tree

Just 2 issues: 1) what should we do say for example we get 5 Yes and 2 No for a single final leaf node attribute? 2) How to post prune using Chi square test?
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17 views

Forcing the order of variable in a decision tree in R [migrated]

I would like to build a decision tree in R. Is there a function or workaround, to specify the order of the variables in the tree? For example if A, B and C are inputs I would like to say: split first ...
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18 views

Iterative Dichotomiser 3 (ID3) decision tree analysis for a small sample size

I am preparing a project which lacks sample size. The data set has only 51 observations. There are 7 predictor variables and the outcome is a dichotomous variable (Yes/No). I am applying ID3 for ...
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1answer
25 views

How can I read this decision tree

I'm sorry, I'm trying to learn some statistics in order to help someone I care about. Here's my problem, I have this decision tree below, for which I have no documentation to help. I cannot find out ...
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18 views

Decision Tree Modelling

Can anyone explain me about Decision tree parameters - minSplit, minBucket, Complexity, minDepth with some simple decision tree example? And how this parameters will affect the accuracy measure? ...
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1answer
24 views

Interpret the (visual) length of the branch in decision tree?

I'm using the tree package in R to produce the following tree. The (visual) length of the first split is huge, and I wonder if this signifies something? Perhaps how ...
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16 views

What is “fitted function” in the context of boosted regression tree?

I'm following the tutorial of package dismo's boosted regression tree, which produces two graphs, about fitted function and ...
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1answer
13 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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1answer
34 views

Posterior probabilities with decision trees or decision forests

Is there a way to get posterior probabilities $P(C | \vec{x})$ (probability that a data item $\vec{x}$ belong to one of the given classes) in a multiclass classification problem using decision trees ...
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7 views

DecisionTreeClassifier scikit-learn : knowing the leaf to which an example belongs to

I am currently reading this paper http://quinonero.net/Publications/predicting-clicks-facebook.pdf , where they are using trees to generate feature that are afterwards fed to a linear classifier. My ...
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1answer
37 views

Visualize difference between 2 classifiers

I trained 2 binary classifiers with the same data (a Decision Tree and a Random Forest). They both made a prediction on the same test data. Now, I want to visualize the difference in classification ...
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6 views

Relative magnitudes of mean squared errors in cross-validation and test data for large regression trees

When pruning a regression tree using cost-complexity pruning, is there any reason to expect that the mean squared errors for the cross-validated data is larger than the mean squared errors for the ...
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17 views

fragmentation problem in decision tree

I am taking a NLP class, in which it says decision tree has the fragmentation problem. It says ...
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23 views
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1answer
32 views

Number of variables for decision trees

I have a data with just 5 independent variables and a response. I am dealing with a classification problem. Will decision trees perform well or the number of variables have to be higher to get ...
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6 views

Decision Tree In Power System Implementation

I am completely new in Machine Learning and very interested to be familiar with its nice approaches which would help me in assessing the behavior of any system. At the moment I am going to focus on DT ...
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43 views

Using an RMSE with derived confidence interval, to generate a prediction interval for an estimate

Previous questions have asked about creating prediction intervals for estimates derived from random forests or boosted regression trees, in a similar way to is easily achieved with linear regression ...
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11 views

Need Insight on Measuring Variable Effect

I apoligize in advance for the wall of text. I'm working on a project in which I have been asked to determine what factors influence employees leaving our company. Also, it would be useful to be able ...
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12 views

Why does the regression model tree consider only those parameters along the path for the model?

When we try to model a regression tree by the M5 approach, while building the model at a particular leaf node, we consider only those variables that have been on the path from the root node to this ...
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26 views

What is the difference between the M5 regression model tree and the Cubist method for regression?

I am aware of how the M5 regression model trees work. I know that they fit linear regression models at every leaf of the regression tree and that every parent in the node is also associated with a ...
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19 views

using SAS for decision tree

I am quite new to SAS. I wanted to figure out how we can use Test dataset and Train dataset seperately. As of now i was dividing the existing dataset into Training and Test dataset. My requirement is ...
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29 views

How does rpart in R differ from SPSS classification trees?

I am using rpart in R for some decision trees. I decided to check the results in SPSS - classification - trees, and different variables were selected. In rpart, I'm using method="class" and the ...
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1answer
63 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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2answers
107 views

How can I get more precise regression tree?

I am a complete newbie to regression trees so maybe I am not understanding it properly. I got the following tree from my analysis (function tree() from R package ...
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49 views

Interpreting output from cvFit(), understanding cross-validation in classification tree model

I am trying to understand how to interpret the output for cvFit(). The data is from UCI's ML repository. This is my model ...
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15 views

Degrees of freedom when splitting populations

I have a question relating to degrees of freedom when I have a number parameters in a model for a population, and then I want to split the population. Say for a certain population I have a the ...
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1answer
28 views

Suitable function to choose the best split in a regression tree/oblivious tree

My main objective is to construct a regression (decision) tree. It is a part of a boosting algorithm using additive regression trees. The first question is what other functions (other than least ...
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13 views

tree - recursive partitioning

I am trying to understand recursive partitioning. I am simulating data. I am curious as to why, subsetting on var2, the coefficient estimates for var1 and var3 are becoming similar to those of the ...
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2answers
74 views

Can C4.5 handle continuous attributes?

I'm trying to play with the breast cancer data available through UCI: https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data When trying to classify the data ...
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1answer
39 views

10-fold cross-validation (high variation)

I am using 10-fold validation method to validate my model. I am using CART model and my sample size $\approx$ 50. Features $\approx$ 9. The 10-fold validated accuracy (averages) is about 76%. However, ...
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59 views

time varying continuous covariate with one single binary outcome

Despite having only a single binary outcome for each ID, there are multiple correlated measurements for the same test for each ID at different timepoints. The individual ID´s are obviously ...
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8 views

Tree classifier or nested model?

I am new to statistics and was wondering what the right kind of model to use for the following scenario. I have two sets of continuous observations A and B for 50 samples. These 50 samples are ...
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1answer
37 views

longitudinal dataset with multiple binary dependent variables

I have a dataset where a test (a continuous variable) is administered every 3 months for 2 years for most of the participants (approx 25% have one or two missing scores). There are further ...
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1answer
125 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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17 views

Fuzzy search in the database

Did anyone have experience with fuzzy search in database? Let's consider some type of Akinator. At each moment the idea is to choose the question which would split all possible (remaining) final ...
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2answers
34 views

Analyses of mixed variables

I'm relatively new to statistics, and am currently working some data collected as a part of an interview survey. I have a response variable in ordinal form, which mostly looks into people's ...
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19 views

Continuous variable evaluation in decision trees

I was going through the C4.5 and ID3 algorithms used to construct a decision tree. Was wondering if there is an efficient way to compute information gain from a continuous variable (during the step ...
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5 views

Dependent predictors with converse effects on the target

I am trying to create a predictive model for marketing in the natural gas field. The model is supposed to guess how probable it is to make a contract in that particular building given many internal ...
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13 views

Using all the data for a decision tree prediction

When doing regression analysis with training and test sets, I would fit the model to the training data. When happy with the model fit I'd then run it against the test/holdout set to get a true ...
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16 views

What statistical test(s) should I run to analyse a directed-tree like data?

Suppose I have data of a kind of binary tree format. There are three levels in the three, thus four different leaf nodes. I would like to find the optimal path from the root node to one of the leaf ...
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58 views

VC-Dimension of n-node binary decision tree in N-dimension feature space

Given input feature space $\mathcal{X} =\{0, 1\}^N$ and output label space $\mathcal{Y}=\{0,1\}$ , prove that the VC-dimension of a binary decision tree with $n$ nodes is in $O(n\text{log}N)$. I've ...
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105 views

How to split a decision tree when information gains of all attributes are zero?

The textbook tells us that we should choose an attribute with the maximum information gain to split a decision tree. My question is what if all information gains are zero? Should we stop splitting or ...
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32 views

Query on Quants

I am working on decision trees for the first time at job. I have done lot of research on CHAID and CART algorithms but find different answers to a very simple question given below : What kind of ...
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1answer
29 views

Mob model tree algorithm

I am trying to figure out the inner workings of the mob function in the party package. I can't figure out how the splitting variable is selected when it is a categorical variable. In the publications ...
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11 views

number of nodes in an unpruned decision tree

What is the number of nodes in an unpruned decision tree that is trained using n samples and that grows until there is only one sample in each leaf? I would like to know if there is a formula to ...
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54 views

Sample Weights for classification using Gradient-Boosted trees?

How can "weights" be given to different samples according to their relative importance while using Gradient boosted decision trees for classification? How does the ...
4
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1answer
92 views

Are Auto-Associative Regression Trees Distinct from Auto-Regressive Trees?

After some reading in the field I was confused as to whether these two models are distinct or really the same. I'm just looking for a simple yes/no with a brief explanation. Note that ...
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1answer
221 views

rpart complexity parameter confusion

I'm a little bit confused on the calculation for CP in the summary of an rpart object. Take this example ...
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96 views

How are CP (Cost Complexity) values calculated in RPART (or decision trees in general)

From what I understand, the cp argument to the rpart function helps pre-prune the tree in the same way as the minsplit or minbucket arguments. What I don't ...
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31 views

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 ...