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

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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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High dimension Categorical Decision trees, Python? [closed]

I'm just getting into the whole machine learning thing after much reading.. I'm trying out This contest on Women's health http://www.drivendata.org/competitions/6/ There are about 1,000 columns of ...
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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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25 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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1answer
19 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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16 views

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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30 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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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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44 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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21 views

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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53 views

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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24 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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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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25 views

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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238 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?
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number of rules from a classification tree?

I have generated a classification tree for a dataset using classregtree method in matlab. Tree gives me rules explained by if and elseifs. For a tree, i want to calculate number of rule generated from ...
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what do you think about the capability of decision trees on the data labels are the sum of features?

Let me assume a data where each instance has a label that is the sum of its feature values. I believe in such a easy problem decision trees have very hard times. What do you think for such an argument ...
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Represent decision tree as matrices

Is it possible to use matrix operations to generate node membership for decision trees? In a binary decision tree, each node represents a condition for a single variable. Ignoring the more complicated ...
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How to fit Decision Tree classifier for highly imbalanced response variable?

I use R, Party package in order to fit prediction model ("classifier") for "Converted.clicks" as response variable. The rest of vars are used as explaining variables in the model. Here is the ...
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42 views

What do these decision boundaries indicate in random forest and svm?

I was working on data science harvard homework problem. It is a two class classification problem in which they plot the decision boundary for random forest, svm and decision tree. The problem has 2 ...
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Understanding cost-complexity pruning in regression trees

I am trying to understand how an optimal tree length is determined using cost-complexity pruning. Visit this lecture note screenshot: ...
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2answers
49 views

Predictive Model - Increase Pediction Accuracy for Less Likely Events

I am trying to build a model that predicts the which binary category a respondent belongs to (0 or 1). I have demographic variables (all categorical) and a few 10 point questions. I have built a few ...
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1answer
58 views

How does caret handle factors?

I have been testing conditional trees and random forests with caret, and I've noticed it does something weird with factors. So, for example, a ctree using the base dataset ...
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Decision Tree English Rules and Dependency Network in MS SSAS

I created a Decision Tree model in Microsoft Analysis Services (SSAS, Visual Studio 2010). There are two tabs in the Mining Model Viewer tab: (1) Decision Tree that shows a tree itself, and (2) ...
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Why is MeanDecreaseGini over 1 in RandomForest package in R? [duplicate]

I am using R package randomForest, and calculated MeanDecreaseGini as below. ...
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46 views

How are individual trees added together in boosted regression tree?

I'm reading Introduction to Statistical Learning, James, G., et al. (2013), in which they describe the Boosted Regression Tree algorithm as following. What I do not understand is Eq 8.10 and 8.11. ...
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37 views

Extract training data predictions from rpart

I'm wondering if there is any method to extract the class assignment of each sample in an rpart model from the training data? E.g. in R using random forest to get the predicted class of each sample ...
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Decision tree with adaboost

Helllo! I'm currently learning the AdaBoost algorithm to use it with Decision Tree. I want to implement everything myself (that's the way I learn - implement everything from scratch and later use ...
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1answer
28 views

Using a set as a feature in decision tree classification

I'm faced with a data set where one of the features is a set of 4-5 categories (this number of categories isn't constant). I need to use this feature for building a decision tree. I searched online ...
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Is it possible to use SD instead of entropy?

While discussing about decision trees in class, my teacher touched upon the topic of entropy. I have understood the purpose of entropy (have not understood how the formula $H(X)= -\sum_{i}{p(x_i) \log ...
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What statistical software does NOT provide for Classification and Regression models [closed]

Appended is a list (as far as I can tell) of statistical software that DOES provide for use of Classification and Regression Tree (CART) models. Some have the CART as a trivial case of things like ...
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Classification problem-Big Data and simple decision rules: logit regression, LDA, random forest, cond. trees, or something else?

This is a big data question from someone who is more accustomed to small data. I would like to develop some classification "rules of thumb," that is, some simple decision rules or a decision tree ...
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Finding the most relevant predictors (features) to build predictive model

I am building a predictive model using CART. I use features (X1,X2, .. X20) and Y as a target. How can I decide which are the most relevant predictors (filtering correlated and features with less ...
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Why to use Chi2 instead of accuracy in a decision tree?

Why many decision trees are using Chi2 or Information Gain Ratio to split the node when they can directly use accuracy, lift or AUC?
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64 views

Is Uplift Modeling a solution to Multiple Comparisons problem?

If you want to identify a particular user segment for whom an experiment produced some lift or incremental effect over the control treatment, wouldn’t it be more direct to do uplift modeling? Uplift ...
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1answer
74 views

Chi-Squared significance test for stopping criteria in decision tree

Going through the paper of BFTree(Best First Decision Tree) from (Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ). I read for pre-pruning do a local attribute selection. And the ...
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1answer
107 views

Questions about decision trees

I'm analyzing decision trees on a regression problem with 12 attributes, a class attribute that can have values between 1-10, and 6497 records. Here is the data. I am using 10 fold cross-validation ...
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How can I plot this graph in R? [closed]

I'm reading "An Introduction to Statistical Learning" and noticed the following plot made in the book for a regression tree: I'm trying to make a similar plot for my dataset but can't figure out ...