'Classification And Regression Trees'. CART is a popular data mining technique.

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How does Quest differ from, for example, C&RT or C5.0?

SPSS Modeler has implementations of a number of decision tree data mining algorithms. Some of them are relatively well-known, such as C&RT and C5.0, some slightly less so, such as CHAID and QUEST. ...
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Difference between One Rule and Learn One Rule

What are the differences between One Rule and Learn One Rule? Learn One Rule goes deeper in the decision tree (and, like 1R, tests on all the attributes), is this correct? Thanks!
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Regression trees - how are splits decided

1) How do we decide if we split into 2 subnodes, or more? Or is it always 2? 2) How do we decide what threshold is the cutoff? Specifically, you have a continuous variable, do you do a binary search, ...
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Deciding Minbucket size in R

What is the criteria for selecting minbucket size in CART in R? Is there any formula for choosing this, based on the number of observations?
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Adding variables to rpart stops the algorithm from splitting (returning base node)

I'm trying to figure out how exactly rpart determines the optimal split. I've read the documentation which states that it optimises for maximal reduction in impurity (information or gini), but it ...
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31 views

Plot a subtree from a big decision tree

I am working on my thesis using decision trees. I am presenting the resulting tree to show how they help in exploring data. My issue is that since the tree is big, I want to break it down into parts, ...
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Building a hierarchical classifier

I'm trying to build a hierarchical classifier on R. I know my data has a tree structure with a root node + 3 levels and that each node on a level has 10 nodes coming out of it. ...
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20 views

MCMC sampling of tree structure

I want to do Metropolis Hastings sampling of a tree structure $\mathcal{T}$ (in the space where the tree size is fixed) from a posterior distribution: $p(\mathcal{T}|X) \propto p(X|\mathcal{T})p(\...
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How to calculate entropy for a specific attribute?

This is super simple but I'm learning about decision trees and the ID3 algorithm. I found a website that's very helpful and I was following everything about entropy and information gain until I got ...
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24 views

Classification Problem with Big Unbalanced Data

I have a dataset with over 1 million observations, and a few dozens of predictors. My target variable is binary (gold customer vs. not gold customer) and I wish to build a classifier for prediction. ...
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Understanding Plurality Voting in Ensemble Methods

I'm reading Data mining with decision trees by Rokach, and i've got to a chapter about ensemble methods (using multiple classifiers) and this is where I can't wrap my head around this concept of ...
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1answer
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Explanation of different testtype and teststats in ctree in party package of R

I was looking into the ctree function in the party package for R. In the ctree_control ...
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1answer
38 views

preprocessing and input format for gradient boosted trees

I'm using MlLib library de Spark and trying to perform Gradient-Boosted Trees algorithm on my data, that has mostly categorical features (and just two numerical features). in the example given in ...
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XGBoost - Can we find a “better” objective function than RMSE for regression?

If we think back to linear models for a moment, we have Ordinary Least Squares (OLS) versus Generalized Linear Models (GLM). Without going too in-depth, it can be said that GLMs "improve" upon OLS by ...
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manipulate text edge labels ctree [migrated]

Got a ctree with four labels, but the categories are long text therefore just the first is shown. ...
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Comparison of decision trees in R cran

Is it possible to compare two (or more) models of decision tree obtained with ctree on different data? In particular I would like a statistical test that compares two decision trees (builded with the ...
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1answer
25 views

Classification and Regression Trees

I would like some insight and some typical "rules of thumb" I can use to decide when to transform continuous predictors into factors and vice versa for classification and regression trees. Should I ...
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gradient boosted cart models - paradigm

Disclaimers: This is a question about the model, not the software. While I was exposed to boosting in my undergraduate program (2007) I have been using boosted trees on and off for the last ~4 years. ...
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Decision Tree with High Specificity and Low Sensitivity

I ran a decision tree on some data with over 1 million of observations, a binary dependent variable and a few dozens of independent variables, some partially correlated. I got weird results. The ...
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When do you use random forest over decision tree?

What criteria make you decide to use random forest over decision tree ? How do we decide when decision tree is not sufficient ?
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Are Logistic Model Trees robust to imbalanced datasets?

I am trying out Logistic Model Trees (Weka implementation) with very imbalanced datasets. Is anyone aware of their robustness without the need of preprocessing steps (such as SMOTE)? My preliminary ...
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52 views

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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1answer
24 views

Decision Tree Giving Impossible Splits

I ran a decision tree in SPSS, using the CHAID method. The result was a tree with many nodes. Some of the splits were impossible. For example: for a variable that is from 0 to 10 (in %), a split was >...
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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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Scaling the data in a decision tree changed my results?

I know that a decision tree doesn't get affected by scaling the data but when I scale the data within my decision tree it gives me a bad performance (bad recall, precision and accuracy) But when I ...
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Predictive power CART pruned vs. unpruned tree

I have a large sales dataset and was intending to use a CART tree to predict the sales price of each item depending on lots of input factors such as the sales region etc. To achieve this I used the ...
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Categorizing Continuous Random Variable in Logistic Regression

I have a Bernoulli response variable and I am going to fit a logistic regression. One of my independent variables is a continuous random variable and I would like to categorize it before fitting the ...
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Ordered sets vs randomly sorted sets: if the subset c(i,i,i,i) is random, is it possible to make the same prediction for the whole subset?

The "compared", "random", and "day" variables are categorical. Here is the sorted data: ...
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Can a classification tree model “know” to predict only one of every class for every subset in a data?

In order to help recipients understand my question, there will be context added. I don't know a whole lot of semantics so please bare with me. Draper is hosting a competition on Kaggle to classify ...
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1answer
38 views

Text & Data Mining Techniques for Twitter data

I have been using Statistica tool to do text mining analysis for Twitter data. Can any one tell me what kind of analysis (Text & Data mining) we can do with Twitter data in general. I am very much ...
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How to train decision tree with adaboost?

I am trying to implement decision tree with adaboost. I understand how the normal decision tree works with the entropy formula. I also understand how adaboost works. But i dont understand how adaboost ...
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33 views

Relation between calculated probabilites and Decision Tree

I'm currently building a classification model in MS azure with the Two-Class Boosted Decision Tree algorithm. From my basic knowledge I know that the decision tree splits the features by a cut value ...
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45 views

Multiclass classification question

I am working on applying Random Forests to a multiclass classification problem, where I have a set of 11 predictor variables and a response that can take the values of "Yes", "No", and "Maybe". In my ...
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How to mitigate the hierarchical error propagation in tree-structured classification

Suppose we have a multi-class classification problem, where the number of classes $K \geq 3$ We use a tree structure of multiple SVMs to divide and conquer the problem, with one example in the figure ...
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Boosted Trees classification

I'm using R's gbm() package to do a boosted classification problem, where my response variable is a binary variable taking values of 0 and 1. I have 11 predictors in my data set. After running the gbm(...
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Explaining the numbers in a decision tree

Using the famous Iris data set with Julia decision tree classifier I get the following tree. ...
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Why is my classification tree predicting only a few classes but not every class?

My dependent variable is a categorical variable with 8 categories. I use rpart to fit a classification tree. The output tree's terminal nodes predict only categories 1, 3 and 7. It says nothing about ...
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How does tree algorithm choosing which node to split?

I know the idea is to choose the split that most improves the loss criterion. In the case I'm interested in, it is the square-error loss. Now how do you get to the condition $$ argmax_{1 \leq m \leq M}...
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What are the main differences between regression trees and model trees?

I am currently carrying out research into decision trees for regression problems. In the literature for the M5 algorithm I have noticed that it mentions that "model trees" and "regression trees" are ...
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35 views

Feature selection step before decision tree?

I want to use rpart (a R package) to build a decision tree model. The data is a high-dimensional expression matrix, with ~50,000 predictors and ~500 samples. The response is a categorical variable. ...
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Saving and loading Regression Tree

Please, could you tell me how to save the tree below in a file and then load it in another session: Model = as.data.frame(aMatrix) train = sample (1: nrow(Model), nrow(Model)/2) tree.model =tree(y~....
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Weights in adaboost/decision tree(cart)

I'm trying to implement adaboost using decision trees. But I'm confused over the weights. I am unable to understand how to incorporate weights in training process, how the formulas for node entropy ...
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Gini impurity and generalization error

Has anyone seen papers on relationship between information-based criterions (such as Gini impurity, information gain etc.) and generalization error? Is there theoretical justification of using such ...
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Gbm.plot y axis

I am fitting a boosted regression tree to count data. The response is distributed Poisson. When I plot the model's partial residuals using gbm.plot, the y-axis goes from -1 to 1. Are these plots ...
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1answer
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In a CART model, why is the average of the leaf proportions equal to the total proportion only when the classes are unweighted?

Suppose I want to do binary classification (the two classes are 0 and 1) and I choose to work with a CART model. I first fit this model on a training set. (Note that I am using Python, and ...
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25 views

Do Random Forests use boosting

Ok so I think I have listened to a few wrong discussions on random forests because now I have a very confused question. With respect to Random Forests and bagging/bootstrapping, I'm good there. The ...
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Using Decision Rules to Make Cluster efect

I have a data set with 3 independent variables and 1 dependent variable. Dependent is play_golf Independents are Humidity, Pending_Chores, Wind I want to create "clusters" of rules and aggregate ...
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42 views

(Boosted) regression trees versus model trees - rule of thumb what to use when

I apply (boosted) regression trees to build predicitive models with continuous outcome (xgboost and gbm). While regression trees ...
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The formal proof of purity gain (information gain) formula for decision trees

Suppose we are constructing a binary decision tree, and are using gini impurity (purity gain) to choose the best feature for splitting a node. We also have only binary features and only two classes. ...