Stands for 'Classification And Regression Trees'. CART is a technique for developing a tree model (T) to predict categories (C) and/or continuous values (R) by recursive partitioning. It does not make restrictive parametric assumptions. CART is a popular data mining technique.
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30 views
Graph data from an Excel Pivot in a Tree Representation
I've been looking for a while to see if there's any add-on or software which allows data from an excel pivot to be graphed dynamically in a visual tree representation. I know of the likes of graphviz ...
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38 views
Interpreting output of 10-fold CV on classification tree
Using info from Decision tree model evaluation for "training set " vs "testing set " in R , I was able to run a 10-fold cross validation on my entire dataset, using this command:
...
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61 views
MS business intelligence development studio CART analysis
I'm doing some clinical database research and in an effort to lessen the burden on our statistical staff, I started to look for different software solutions to get the analyses I need, and that's ...
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45 views
Variables selection (continuous and classification): how to do in R?
My dataset have both classification (categorical) and continuous variables, ~ 300 variables in all. I'm looking for a way to reduce my attributes to be less than 300 and put them in the decision tree ...
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49 views
Deciding attributes for decision trees
I'm a complete beginner when it comes to R and decision trees, but I was asked to take a look at this to see if this was a viable solution for my data. So please excuse me if I say completely wrong.
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1answer
76 views
CART - Classification And Regression Trees
I try to prune one regression tree build with the rpart function in R.
To decide where to prune the tree I used the plotcp function. But I noticed that if I use the same predictor variables and in the ...
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1answer
36 views
How can I calculate leaves and nodes of a C 4.5 decision tree?
I have a given amount of attributes, for example 1024, and now want to calculate the amount of leaves and nodes C 4.5 produces (approximately).
Is there something like that for binary trees - a ...
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1answer
88 views
How are categorical variables used when fitting a decision tree in scikit-learn?
I am used to R, in which you can use factor(variable) to indicate a categorical variable. However, in scikit-learn, trying to pass a variable of strings causes the DecisionTreeClassifier to give an ...
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2answers
206 views
Practical questions on tuning Random Forests
My questions are about Random Forests. The concept of this beautiful classifier is clear to me, but still there are a lot of practical usage questions. Unfortunately, I failed to find any practical ...
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1answer
54 views
Log transformation in CART analysis
I'm working as a liaison between a researcher and a stats team at a university. I'm a database admin who is working on using business intelligence tools to offer the option of (as of right now) offer ...
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32 views
Approximation with CART
In R. Genuer PhD thesis is mentioned (p 90-91) that the curve of standard deviation of Variable Importance's is used to fit a CART model to that curve. So what is CART approximation as in figure 3.7 ...
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29 views
Is there a theoretical basis for the shrinkage used in Boosted Regression Trees?
In Gradient Boosted Regression Trees, a shrinkage $\nu$ is often applied as:
$$ f_t(x) \leftarrow f_{t-1}(x) + \nu h(x)$$
where $h$ is the regression tree learned by fitting the tree to the gradient. ...
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1answer
101 views
Building the dataset for Random Forest training procedure
I should use the bagging (bootstrap aggregating) technique in order to train a random forest classifier. I read here the description of this learning technique, but I have not figured out how I ...
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26 views
how to deal with a very predictable variable - but very small sample - should I keep them ?
I am doing modeling for say response y ~ a+b+c+e+f.. x+y+z ( say I got model for decision tree )
if my dataset is big, say 1M records :
I have a-z independent variables , but having x,y,z are ...
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66 views
Model Trees: M5, Cubist - What about the assumptions of the linear models being used?
From what I understood, these models differ from CARTs for regression, mostly because they fit a linear model at the leaves of the tree instead of simply taking an average. They also "smooth" the tree ...
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110 views
How do BART (Bayesian Additive Regression Trees) work?
I am confused about two concepts about the BART model:
How is each tree created?
i.e. is a random sample taken from the training data and the tree built from that sample (as in random forests), or ...
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1answer
203 views
Decision tree model evaluation for “training set ” vs “testing set ” in R
So I got my training set with 70% of my data called "train" / 30% "test"
I use ctree to get my decision tree model with something like this code below :
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61 views
Determining optimal height for regression tree
I have a data set of approximately 400,000 records (for those of you who know, the data set is the one provided by yahoo for their yahoo learning to rank challenge). From this data set I learn a ...
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1answer
150 views
mobForest R Package
I have recently begún to learn about model based recursive partitioning by playing around with MOB in the party package. I came across this mobForest package but am a little baffled towards what it is ...
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36 views
fitting the tail of a distribution in a regression tree
I have 3 integer valued time series $a_t$, $b_t$ and $y_t$ with $k$ observations. I want to fit $y_t$ with the 2 first, and for that purpose I use a regression tree like this:
test all combinations ...
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357 views
Weka J48 decision tree problem
I have a CSV dataset which contains mean (Numeric), spread (Numeric), review (string), ...
2
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1answer
194 views
What is “feature space”?
What is the definition of "feature space"?
For example,
When reading about SVMs, I read about "mapping to feature space".
When reading about CART, I read about "partitioning to feature space".
I ...
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1answer
159 views
Predict sales levels with decision trees
I need to build a model using climate variables (temperature, rainfall) to predict
monthly sales (horizon of 6 months) for certain product. The data has strong seasonality and a standard regression ...
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1answer
133 views
Interaction depth parameter in GBM
In the GBM package one is supposed to be able to provide interaction.depth>2, which means higher-order interactions between features.
However, the resulting trees (as seen by pretty.gbm.tree) never ...
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35 views
How to discretize training and test files to not encounter inconsistency in data?
I have a problem in discretizing my data. My data comprises of two parts: training data and test data. I discretized training data using some parameters of equal-width binning and used the same ...
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1answer
48 views
Multi-way split with CARTs
I am predicting a categorical variable (Yes/No) called Company Failure. This depends on a range of input variables (financial ratios).
I am planning to convert a continuous variable (Liquidity Ratio) ...
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1answer
196 views
how to interpret reading decision tree result from ctree() in r?
After running ctree(model)
My result look like this below :
...
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1answer
114 views
Error while calculating metrics like AUC, ACC in R
I am trying to make predictions on my validation data using a decision tree model created with training data set. I am able to do that with success but, I am not able to calculate various metrics like ...
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1answer
55 views
Can I represent a non-boolean function with a TREE?
I have a question in respect to tree representation of a non-Boolean function.
Is there any problem in representing a function from
space
Boolean->Limited Integer
or
Limited Integer->Limited ...
3
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2answers
249 views
Mathematics behind classification and regression trees
Can anyone help explain some of the mathematics behind classification in CART? I'm looking to understand how two main stages happen. For instance I trained a CART classifier on a dataset and used a ...
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575 views
Interpreting decision trees outputs in R?
I have created 2 decision trees, I just want to assure if I am making correct interpretation about it. Here is my first tree:
Note: Right click on image and select view image to view it clearly!
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1answer
219 views
Classification and regression trees (cart)
In matlab, classregtree can be used to implement classification and regression trees (CART) you can find this in the documentation however it's not clear what ...
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3answers
280 views
Which type of regression fits better?
I am a newbie in data mining world. I have a general question.
I have a data set which has 10 independent variables and one target variable named as category which has 9 values like: 1, 2, 3, 4, 5, 6, ...
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1answer
171 views
How to use CART for AdaBoost?
I am trying to use CARTs (Classification and Regression Trees) for AdaBoost as weak learner. My question concerns the update of the weights after fitting the best weak learner.
A single CART node ...
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107 views
Decision tree of CSV file is different from that of the ARFF file
I use Weka 3.6.7 for a classifying problem. I always convert my dataset into CSV format and then use the Java class csvLoader (provided by Weka) to convert it to ...
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1answer
196 views
Calculating the K-fold cross validation confusion matrix
I know this is a dumb question, but I can not find an answer (I guess everyone else knows this). I want to verify:
When I do k-fold cross validation of a decision tree, the software produces a ...
3
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1answer
129 views
ID3 and C4.5: how does “gain ratio” normalize “gain”?
The ID3 algorithm uses "Information Gain" measure.
The C4.5 uses "Gain Ratio" measure which is Information Gain divided by SplitInfo, whereas SplitInfo is high for a split where records split evenly ...
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63 views
Growing a classification tree manually
I want to create some CARTs in R. I used Clementine a while ago for this purpose and remember I could go into "manual mode" and grow the trees by hand. I could manually choose which variable to use ...
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63 views
Classifiers with post-training constraints on the prediction space
I'm familiar with using tools like SVMs and decision trees for discrete classification problems. But one detail that I have not encountered in that domain is: what do you do if your classifier must ...
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1answer
1k views
How to actually plot a sample tree from randomForest::getTree()?
Anyone got library or code suggestions on how to actually plot a couple of sample trees from:
getTree(rfobj, k, labelVar=TRUE)
(Yes I know you're not supposed ...
3
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1answer
75 views
Can two or more splits in a binary decision tree be made on the same variable?
My question is about a binary decision tree (binary to integer).
Is there any problem if the conditions defined on a same variable ex. x1?
I mean when i define the variables for my tree, can I ...
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302 views
Choosing complexity parameter in CART
In the rpart() routine to create CART models, you specify the complexity parameter to which you want to prune your tree. I have seen two different recommendations for choosing the complexity ...
2
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1answer
162 views
k nearest neighbor with decision tree
A dataset has a few attributes. One of the attributes(attribute X) represents a distance with values expressed in meters. I use cross validation to estimate the performance of Decision tree and k ...
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98 views
Is it possible to have xerror increased in a tree using rpart?
I am new to R and rpart package. When I plot the tree using rpart:
...
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317 views
Information gain and information gain ratio: Do I have to pick just one?
When splitting attributes while constructing a decision tree, i can use information gain or information gain ratio to try and determine the best value to split the tree on.
I'd use information gain ...
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70 views
CART with rpart and a 12 level factor
I have a 12-level factor variable (month) in my dataset and I wanted to fit a CART tree with rpart(). Would you split the 12-level factor variable into 12 dummy variables?
If I fit the model with one ...
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2answers
184 views
Is it possible to build a more “controllable” decision tree like below?
I have 2 real and 1 discrete input variable whereas the output variable takes either of the 2 nominal values (i.e. 2 class problem). First I used Weka to train C 4.5 decision tree in a 10-fold cross ...
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36 views
Does the greedy approach only affect pruning?
In ID3, the attributes are split based on Entropy.
The attribute and the split value of that attribute that will reduce the Entropy the most are chosen.
I understand that the motivation behind this ...
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1answer
458 views
How to decide which decision tree classifier to use?
I am confused about which decision tree algorithm in weka to use for my application. I have 5 real input variables and 2 classes. In various online tutorials J48 (C 4.5) seems to be the algorithm of ...
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1answer
234 views
CART (rpart) balanced vs. unbalanced dataset
I am fitting a tree (CART) to the olives-dataset. The training data has 436 observations (test data: 136). I have 3 responses (the 'Region' variable) which splits the training data into 116 / 74 / 246 ...
