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

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rpart classification: why is my predict() output not adhering to type=“class”?

I have a dataframe, 'datas', with 200 observations and a series of columns (some numeric, dummy, etc) and a binary class variable to be predicted that is called "bad_econ." I would like to get the ...
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11 views

Method comparisons for trees and forests

I am looking to compare various types of models for a dataset, in order to determine which one is the most suitable. They will all be a form of decision tree, from the basic tree to random forests, ...
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37 views

Modelling clustered data using boosted regression trees

I'm modelling habitat selection using boosted regression trees (BRTs), which I prefer over linear models for a variety of reasons (modeling complex nonlinear relationships and interactions, ...
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1answer
24 views

Size of terminal node in decision tree/random forest?

I am having issues trying to understand what does the size of a terminal node in a decision tree means? Could anyone give me an easy explanation? I know a terminal node is a leaf node, the one that ...
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16 views

Tree not predicting all factors

I'm using the "tree" function to fit a model to predict a factor "Output" based on A, B, C, D and E ...
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91 views
+50

Relative variable importance for Boosting

I'm looking for an explanation of how relative variable importance is computed in Gradient Boosted Trees that is not overly general/simplistic like: The measures are based on the number of times a ...
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1answer
31 views

Classification trees: favor one classification over other

We're using classification trees (c50 package) for a BUY/WAIT advice. However, the advice in our training set is not well balanced. That is, we advice to buy 3/4 times more than to wait. Probably as ...
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1answer
23 views

What are the goodness of fit variables in classification trees?

I have used multivariate linear regression for one of my projects, and used r-square and p vals to evaluate the model. I couldn't find what such metric we would use for decision trees and random ...
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35 views

classification tree with R part

I am trying to grow a classification tree with a few continuous explanatory variables and a few factor variable. It seems the Rpart alogrithm is ignoring the factor variables. The differences are ...
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1answer
46 views

How does feature selection work in Random Forest?

I've been trying to improve the performance of my random forest model, and read the following paper on feature selection using random forest (see algorithm in section IV: Overfitting - A. Feature ...
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1answer
38 views

How to predict new data with a classification tree in R?

I have built a classification tree for factor variables with the rpart package and now I want to predict unseen data with it. How can I get a sense of whether the model is good at predicting unseen ...
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21 views

Gradient Boosting Paper Understanding

I am reading Gradient Boosting paper link. I need some examples to understand the following statement: Suppose for particular loss function L(y,F) and/or base learner h(x; a) the solution to (9) ...
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1answer
47 views

Using bagged ensemble of regression trees, feature selection based on feature importance

I am working on relating aesthetic scores of given images (about 17k training+validation samples and 280 image features) and getting best result using ensemble of CARTs. Beside achieveing a good ...
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47 views

A question about Dynamic Random Forest

On this article, Simon Bernard proposes a new approach for constructing Random Forest called Dynamic Random Forest. I am new on this subject, so after reading the article, I have a doubt regarding the ...
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1answer
38 views

Gini Algorithm for beginners

"Gini Algorithm" when is it used ? I mean what is it used for ? is it used for classification or building the tree ? or something else ? what are the alternative algorithms for the "Gini algorithm" ...
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14 views

cross validation for cart

When a dataset is given and it is divided into N parts, training a Cart on N-1 parts and testing it on the remaining part (and doing that N times, i.e. for each possible leave-out), one ends up with N ...
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1answer
27 views

How do Conditional Inference Trees do binary classification?

I am trying to learn about Conditional Inference Trees, and have been doing some very simple comparisons of the ctree() and rpart() functions in R. I have looked at the documentation for ctree(), but ...
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8 views

Likelihood estimator for JMP decision trees

In the documentation for JMP decision trees- it details that the calculation for the "Entropy RSquared" (Actually McFadden's $R^2$) is calculated thus: $R^2 = -2\log(L_m/L_0)$ Where $L_m$ and $L_0$ ...
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25 views

Classification and regression tree (CART) on large data set

I am trying to approximate a multivariate function $y = f(x_1, ...x_n)$, which I have reason to believe will be well approximated by a classification and regression tree. Some of the variables are ...
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25 views

Decision tree analysis in SPSS

I am newbie in the field of analytics so my question could be naive. I tried searching the answer before posting but could get my hands on the exact answer I am looking for. I have a simple model to ...
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1answer
74 views

How to use decision stump as weak learner in Adaboost?

I want to implement Adaboost using Decision Stump. Is it correct to make as many decision stump as our data set's features in each iteration of Adaboost? For example, if I have a data set with 24 ...
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7 views

How to compute a consensus tree from RandomSurvivalForest object?

Is it possible to compute a consensus tree from a Random Survival Forest object using R? Please note I don't mean Random Forest, but Random Survival Forest. Which R packages are necessary or which ...
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1answer
46 views

Force start with specific root node using decision trees in R

I'm using decision trees in R, with library("party") ctree.dt <- ctree(Pasillo_Ataque_2T ~ Dist_Col_1T + Dist_1T_2T, data=dt) And it produce: It's ...
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22 views

Comparing models on two target variables

I have a dataset with two binary target variables $Y_a$ and $Y_b$. I can build two classification tree models: $\hat{Y}_a = M_a(X)$ $\hat{Y}_b = M_b(X)$ Questions: Is there a way to compare the ...
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26 views

Rule based classification

I have dataset with continuous, ordinal & nominal variables features (v1 to vn) and a binary outcome variable (red/green). The task is to identify top N "rules" that influence/indicate the ...
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8 views

Minimum description length principal (MDLP) stopping criterion with repeated values

Fayyad's paper on discretization of continuous attributes describes a canonical way to discretize continuous attributes using a minimum entropy method. However, the paper does not describe what to do ...
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64 views

Predictions for rpart model require more variables than shown in the classification tree

Using rpart from the caret package, when plotting the final model I get a classification tree that seems fairly simple (6 variables shown in tree). However, when I request the final variables from ...
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19 views

Cutoffs to consider for survival tree

In an tree based algorithm a criterion is measured at certain cutoffs for the variable. This cutoffs are the candidate split points for that variable. How does one come up with candidate split points ...
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99 views

Machine Learning Methods for Binary Classification

I was hoping to get a nice list of alternatives to logistic regression and decision trees for binary classification ("Yes vs. No" or "Cured vs. Not cured"). I am more interested in identifying the ...
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13 views

Performance of Decision tree and Association rule mining with increasing size of training data

I planning to do misconfiguration identification using some available dataset. So I have two dataset, one with enough number of observations, say from 1000 to 3000 and another one with less than 50 ...
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78 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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70 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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2answers
74 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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2answers
88 views

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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48 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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52 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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37 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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1answer
115 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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45 views

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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1answer
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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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1answer
46 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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26 views

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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37 views

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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114 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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26 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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21 views

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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1answer
78 views

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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101 views

“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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65 views

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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84 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. ...