similar to: Decision Trees

Hi! I'm trying to construct and plot a decision tree to class a set of only 8 objects and tried to use the rpart and tree function, but get a error message both times: rpart: fit is not a tree, just a root tree: cannot plot singlenode tree I read in the post 'question regression trees' that rpart doesn't split a set of fewer than 20 objects...so I guess the same holds true for

Hi, I am somewhat new to R so this question may be foolish, but is it possible to add decision trees into a list, array or vector in R? I am trying to build a collection (ensemble) of decision trees. Every time a new instance arrive I need to get the prediction of each decision tree. I have tried to add a decision tree into a variable but without luck. Is a special package needed perhaps? This

Hi, I am using rpart decision trees to analyze customer churn. I am finding that the decision trees created are not effective because they are not able to recognize factors that influence churn. I have created an example situation below. What do I need to do to for rpart to build a tree with the variable experience? My guess is that this would happen if rpart used the loss matrix while creating

Hi netters, I want to learn a decision tree from a series of instances (learning data). The packages tree or rpart can do this quite well, but the scoring functions (splitting criteria) are fixed in these packages, like gini or something. However, I'm going to use another scoring function. At first I wanna modify the R code of tree or rpart and put my own scoring function in. But it

Hi all, Does any of you know how to make a decision tree when the data set contains factors and numericals? I've got a data frame with 3 columns, where y and x1 are numerical and x2 contains factors. Is it possible to use the rpart package, and in that case how? Otherwise, is there another alternative? This is what I've tried so far > rpart(LT50_NA ~ Raf + Antho,

I am familiar with the rpart and tree packages for classification and regression trees. However, quite a bit of the research in the transportation community relating to decision trees uses the C4.5 family of algorithms by Quinlan. Are there any plans to make a C4.5 (or a derivative of it) available to R? If not, then I might use the WEKA Java package ( http://www.cs.waikato.ac.nz/ml/weka) that

Hi Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

See ?rpart.control. I get: > golf.rp = rpart(Outlook ~ ., golf, control=rpart.control(minsplit=1)) > golf.rp n= 14 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 14 9 rain (0.2857143 0.3571429 0.3571429) 2) Temperature< 71.5 6 2 rain (0.1666667 0.6666667 0.1666667) 4) Temperature< 64.5 1 0 overcast (1.0000000 0.0000000 0.0000000) * 5)

Dear mailinglist members, I have the following problem: I run a decision tree using the rpart function and, afterwords, I try to find to which leaf node a new register (not used to build the decision tree) belongs to. I will try to explain better: rpart.tree <- rpart(target.value ~., data) leaf.node <- new.function(rpart.tree, new.register) The new register has all the explanatory values

I still need the output to match my requiremnt in my original post. With decision rules "clusters" and probability attached to them. The examples are sort of similar. You just provided links to general info about trees. Sent from my Verizon, Samsung Galaxy smartphone<div> </div><div> </div><!-- originalMessage --><div>-------- Original message

Newbie question, but I've checked archives etc. Am trying to reproduce in R Quinlan's trivial example of the "golf" decision tree. The data file of 14 examples follows (read in via read.table()): Outlook Temperature Humidity Windy PlayDontPlay 1 sunny 85 85 false DontPlay 2 sunny 80 90 true DontPlay 3 overcast 83 78 false Play 4 rain 70 96 false Play 5 rain 68 80 false Play 6

I'd like to train a decision tree on a set of weighted data points. I looked into the rpart package, which builds trees but doesn't seem to offer the capability of weighting inputs. (There is a weights parameter, but it seems to correspond to output classes rather than to input points). I'm making do for now by preprocessing my input data by adding multiple instances of each data

Tjats great that you are familiar and thanks for responding. Have you ever done what I am referring to? I have alteady spent time going through links and tutorials about decision trees and random forrests and have even used them both before. Mike On Apr 13, 2016 5:32 PM, "Sarah Goslee" <sarah.goslee at gmail.com> wrote: It sounds like you want classification or regression trees.

Since you only have 3 predictors, each categorical with a small number of categories, you can use expand.grid to make a data.frame containing all possible combinations and give that the predict method for your model to get all possible predictions. Something like the following untested code. newdata <- expand.grid( Humidity = levels(Humidity), #(High, Medium,Low)

Have anybody used Decision Tree in Python or C++? (or written their own decision tree implementation in Python or C++)? My goal is to run decision tree on 8 million obs as training set and score 7 million in test set. I am testing 'rpart' package on a 64-bit-Linux + 64-bit-R environment. But it seems that rpart is either not stable or running out of memory very quickly. (Is it

Hello, this question is a bit out of the blue. I am a big R fan and user and in my new job I do some decision modeling (mostly health economics). For that decision trees are often used (I guess the most classic example is the investment decision A, B, and C with different probabilities, what is the expected payoff). We use a specialized software called TreeAge that some might know. The basic

Nope. Random forests are not decision trees -- they are ensembles (forests) of trees. You need to go back and read up on them so you understand how they work. The Hastie/Tibshirani/Friedman "The Elements of Statistical Learning" has a nice explanation, but I'm sure there are lots of good web resources, too. Cheers, Bert Bert Gunter "The trouble with having an open mind is

Dear useRs: I try to plot an rpart object but cannot get a nice tree structure plot. I am using plot.rpart and text.rpart (please see below) but the branches that connect the nodes overlap the text in the ellipses and rectangles. Is there a way to get a clean nice tree plot (as in the Rpart Mayo report)? I work under Windows and use R2.11.1 with rpart version 3.1-46. Thank you. Tudor ...

Hi I'm trying to get the top decision rules from a decision tree. Eventually I will like to do this with R and Random Forrest. There has to be a way to output the decsion rules of each leaf node in an easily readable way. I am looking at the randomforrest and rpart packages and I dont see anything yet. Mike [[alternative HTML version deleted]]

Dear R, Short verse of the question: Is there R code which will calculate surrogate splits and/or delta impurity for decision trees at each node? Long Version: I have local, legacy code which I use to calculate my decision trees. I would like to switch to R, but as I understand it surrogate splits are not implemented. Surrogate splits and feature ranking are described in Breiman et al