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

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Decision criteria in decision trees

I'm not so familiar with statistical models, so sorry if this is a naive question. In training a decision tree with datasets that include categorical variables, the rules are always in from of x = y. ...
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47 views

Building a regression Tree with R FROM SCRATCH

I am trying to build a basic regression Tree in R FROM SCRATCH (I know of rpart, tree, RandomForest,etc.). But it is just something that I would want to code myself for my culture. In terms of ...
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23 views

Sampling : Gradient Boosting Tree

I have a question regarding the algorithm of Gradient Boosting Tree. I understand Simple tree is built for only a randomly selected sub sample of the full data set (random without replacement). Each ...
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1answer
13 views

Making a mob-like decision tree with pre-specified splits and models for leaves

I would like to make a special kind of hybrid tree model in R, similar to the mob models in the party and ...
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15 views

Existing relationship between predictors and response in a classification method

Lets say that there are 3 predictors A,B and C. There is a response Y, which is already related to A,B and C mathematically. Y = ABC. Past data for Y does not exist, and is calculated using the above ...
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Error in gbm.step: In cor(y_i, u_i) : the standard deviation is zero [on hold]

My data is called: data(Normalised.scores) I am attempting to construct a boosted regression tree using the function gbm.step() using the dismo package, where ...
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1answer
20 views

Which parameters to tune in CART?

I am using caret package in R to train CART model. train function seems to tune only the complexity parameter (which in a way determines depth of the tree and number of terminal nodes). Is this ...
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Classification tree greedy algorithm

I have learnt about the greedy algorithm in building a tree. However, I believe that it is "greedy" because at each iteration of the split, we only decrease the impurity locally, rather than globally. ...
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1answer
28 views

Regression Tree Impurity

It seems pretty obvious that if I currently have a tree with a certain total impurity, then by splitting it again optimally, I can never end up with a greater total impurity. It seems similar to the ...
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How can you use a decision tree classifier on nested data?

I have a data model with natural one-to-many relationships. ...
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2answers
34 views

Decision trees in smaller datasets

I have the following dataset from: train <- read.csv(url("http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv")) When I want to make a ...
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1answer
49 views

Understanding decision trees

Im new to decision trees and am trying some decision tree modelling now with titanic data. I have the following dataset. ...
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22 views

Avoiding overfitting with linear regression trees

I use regression trees (R package rpart) in my statistical analysis, and have received a critical comment that this method amounts to a "hunting expedition" that will always produce a result ...
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How can we determine the terminal node size in decision trees?

When we build a decision tree, are there any rules based on which we can control the size of terminal nodes? Obviously, the size cannot be too small. What kind of rules do we have to determine the ...
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62 views

CART with Ordinal Response Variable using rpartScore Stuck

I'm trying to fit a decision tree over some data which has ~40K rows and ~200 features. The response variable, y, is ordinal and takes values ...
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37 views

Compare two hierarchical trees

I want to measure the similarity between two hierarchical trees generated from the same n objects. The two trees are generated using the same metric at two different instants. Thus, I would like to ...
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1answer
73 views

CART: Selection of best predictor for splitting when gains in impurity decrease are equal?

My question deals with Classification trees. Consider the following example from the Iris data set: I want to manually select the best predictor for the first split. According to the CART ...
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1answer
16 views

How can I determine the number of segments my data should be divided into?

Are there any rules behind the number of bins data should be segmented into using the classification trees?
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1answer
64 views

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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16 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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43 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
31 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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1answer
125 views

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
32 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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38 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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46 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
54 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
50 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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24 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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56 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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1answer
51 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
44 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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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
32 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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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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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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42 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
88 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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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
64 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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33 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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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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1answer
85 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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1answer
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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108 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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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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91 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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75 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 ...