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

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Error in pred1.tree(tree, tree.matrix(nd)) : corrupt tree [on hold]

I get the following when running the cv.tree function from the tree package in R. Any ideas? ...
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57 views

How to Balance my Dataset?

I have 90% negative examples and 10% positive examples,(13,000 observations, 90 Variables). my model shows me that the miss classifications error is 0.1 but my confusion matrix shows me that the TP ...
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47 views

Timeseries Regression - threshold value, regular and time-series covariates

i am trying to find a time-series regression or machine learning package that allows the following analysis: Lets assume that ice-cream sales are a function of: a) a threshold value on outside ...
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42 views

Will decision trees perform splitting of nodes by converting categorical values to numerical in practice?

In Decision trees, while doing classification or regression, are we using only numerical values. Suppose if i am having a column of 'Wind' as a feature. Suppose, I am having 5 rows ( observations ). ...
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Hierarchical clustering with agnes - how to cut the tree?

I am working on a data.frame with both categorical and metric variables ...
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36 views

Choosing right range for data while using scikit-learn

I have a dataset with 1175 examples and 21 features which are in the range of [-1, +1], and two class labels 1 and 0. As I read in the most of the resources, it is good to have data in the range of ...
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42 views

Using SMOTE for building CART models

Suppose I want to build an interpretable model and the response variable is 0/1. However, there are 99% 1's and 1% 0's. The date looks like this: ...
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32 views

How to deal with highly unbalanced data

I want to build a CART on data where the response variable is a binary variable, but it is highly imbalanced(i.e. 100,000 0's and 4000 1's). Are there any R packages that allow you to build CART ...
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61 views

Random forest and model predictions

I have a working random forest model (classification tree) in R that I made with a training dataset. I used the predict function with a verification dataset: ...
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RMSE and Decision Trees

I'm running a series of regressions using decision trees and am getting good results, but I've got a question. In the pacakge rpart, you can run cpplot to get a graphical representation of where to ...
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34 views

Decision Trees disadvantage : Inability to examine more than one attribute at a time

I'm reading up on decision trees and in Negnevitsky (2002), one significant limitation on the decision tree is their inability to examine more than one variable at a time. I've been trying to ...
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36 views

Classification trees: the best Predictor and the ability to predict

The topmost decision node, the root node in a classification tree is said to be the best predictor of the model. Does this mean the root node variable is also the best in predicting the target ...
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Transforming nominal attribute to binary for CART

I found the following description regarding model trees for numeric prediction, in which nominal attributes are transformed to binary attributes. Before constructing a model tree, all nominal ...
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Tree analysis - CHAID cart

I am new to CHAID and want to know how to decide which independent variables I should select to run CHAID Analyis? Is there a technique to select and then apply them and run the analysis? Please guide ...
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Predictive model decision tree [migrated]

I want to build a predictive model using decision tree classification in R. I used this code: ...
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93 views

Do CART trees capture interactions among predictors?

This paper claims that in CART, because a binary split is performed on a single covariate at each step, all splits are orthogonal and therefore interactions among covariates are not considered. ...
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22 views

Determine confidence in a CART model with factor (2 levels) response variable (using rpart)

I use the package rpartto model a classification/regression tree. I have the variables $x,y,s$ where $x$ is in $\{-1,1\}$, y is continuous in $[0,1]$ and $s$ is a ...
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Decision trees: “pchance” at split

I'm reading through Prof. Andrew Moore's Decision Trees tutorial, and I have a question: images of the trees all reference a quantity referred to as "pchance" at the graph splits (see page 19). This ...
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why do decision tree packages convert factor variable into two binary variables

Why are decision tree packages like say, rpart slow with increasing the number of factor levels in R. I read that it basically converts each factor variable into two binary variables representing ...
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“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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Interpret learning curves

I am training a Decision Tree on a dataset of around 580.000 data points. I took the following steps: Split the dataset in training (75%) and validation (25%) set. Determined the best depth for the ...
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50 views

Predictive Decision Tree in R

I am currently working on a dataset in R-studio and as the title might suggest I am having difficulty creating the tree I'm looking for. My dataset consist of 122151 observations with 33 Variables. ...
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31 views

Tree model with poisson distributed response variable

If I understand correctly the R tree model - library(tree) - can only be used if the response variable is normally distributed. Is there a way to create a tree model with poisson distributed response ...
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34 views

What is the test statistics used for a conditional inference regression tree?

In Hothorn et al, the test statistic is specified as $$ T_j(L_n, w) = vec(\sum w_i g_j(X_{ji}) h(Y_i, (Y_1,...,Y_n)^T))$$ What is the exact form of this test statistic with a continuous response and ...
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Classification & Regression Trees (CART)

It seems that most CART examples I encounter involve splitting the sample into a training sample and test sample. This split sample method strikes me as terribly inefficient unless you have a large ...
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Training and testing a Decision Tree Learner, can linear regression help obtain an optimal partition?

In class we are seeing decision tree learners and as a project I implemented from scratch my version of the one taught to us. I am using the famous Titanic Survival data (~1000 lines) for training and ...
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Plotcp Plot Plateaus

I'm trying to understand why this graph plateaus. I have many more levels in the predictors than there are splits, so I'm confused why the tree stops splitting at only 447. I'm also confused why the ...
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Why added predictors improved performance in decision trees if they don't appear in the model?

although I've been working some months now with decision trees, I still have issues understanding some things and also finding a right source to answer my questions. Maybe I'm not using the right ...
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55 views

Meaning of `max_depth` in GradientBoostingClassifier in scikit-learn

when I use the GradientBoostingClassifier from scikit-learn, I find that there is a parameter max_depth to set, which controls the maximum depth of the regression ...
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25 views

How to choose a regression tree (base learner) at each iteration of Gradient Tree Boosting?

I'm trying to understand Gradient Tree Boosting, by following Prof. Friedman's original paper: Greedy Function Approximation: A Gradient Boosting Machine. Basically, at each iteration, a regression ...
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Generating a good training dataset for decision tree building

I am building a decision tree predicting the accuracy score of an image processing algorithm, based on a number of image parameters. I have identified 6 uncorrelated parameters that impact the ...
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51 views

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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Advantage of GLMs in terminal nodes of a regression tree?

So I'm playing around with the idea of writing an algorithm that grows and prunes a regression tree from the data and then, in the terminal nodes of the tree, fits a GLM. I've been trying to read up ...
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Do i need equidistant time intervals for classification task?

I have a dataset of the following structure and want to use it for a classification task. The first column contains the timestamp when the values were measured. As you can see only data changes are ...
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Splitting variable selection in regression trees

I am struggling to determine how the further partitioning of nodes in a regression tree works. I have figured out how the initial splitting variable and split point at the root node are selected, but ...
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C4.5 How to select the split point (threshold) for a Continuous Attribute

Using the "play golf" or "play ball" data (listed at the bottom), to pick the root node we look at Outlook, Temperature, Humidity, and Wind, to see which has the highest GainRatio. Now, Outlook will ...
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34 views

When is classification error rate preferable when pruning decision trees?

I'm going through Chapter 8 of "Introduction to Statistical learning" which introduces decision trees. My question is specific to the three approaches to pruning a decision tree (i.e., classification ...
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Tree Pruning Tuning Parameter

Suppose I have grown a full decision tree and now I want to define a sub-tree that prunes back the full tree. Let $T$ be the number of terminal nodes for a given sub-tree, then the pruning ...
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50 views

Logistic regression before decision tree model

I am trying to run several decision tree models (CHAID, C&RT, QUEST), but I have learned that several researchers have applied logistic regression model first in order to select risk factors. So, ...
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why boosting method is sensitive to outliers

I found many articles showing that boosting methods are sensitive to outliers, but no article explains why. In my experience, I feel outliers data is bad for any machine learning algorithms, but why ...
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Should I use Mean Square Error or Classification Rate?

I am a self-taught person and I would like your help. I am learning about predictive modeling in general, and I'm also trying to do predictive modeling for a specific problem. I am exploring ...
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41 views

Correlation between decision trees

How do you find the correlation between decision trees? I know this is an issue when working with random forests, but I can't find an explicit formula anywhere. I only get that random forest reduces ...
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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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46 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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Using regression tree on time series data

I have been looking around for resources on applying a regression tree in an attempt to understanding how various spend variables impact a companies revenue overtime. Is this type of analysis ...
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Decision tree indicating payoffs

I need to draw a decision tree to represents these requirements : The research and development manager in an old oil company, which is considering making some changes, lists the following courses of ...
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242 views

Does the number of rows really matter beyond a point?

While working with any machine learning algorithm, does the number of rows really matter beyond a certain point? I have kept some algorithms(decision tree in this instance) running for days, and the ...
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CHAID tree analysis in SPSS

I am trying to build a decision tree using CHAID in SPSS. I am however getting only one node. I have tried taking several data inputs. I also tried out CRT. Is there some setting that needs to be done ...
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Why do Matlab's TreeBagger and fitensemble with 'bag' and same parameters give different predictions

Matlab's Statistical Toolbox has two bagging tree algorithms implementation: Tree Bagger Fitensemble (see 'Bag' method) I am currently using (1) for a Regression problem. However, I would like to ...
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Do classification trees need to consider the correlation between attributes?

In decision tree classification, we use the attribute that splits records, like entropy, as split nodes. Does it need to consider the correlation between attributes?