similar to: Decision tree and factor variables

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

Hello, My question is about the algorithm used behind decision tree package rpart. It is not clear in the help if the algorithm used is ID3 or C4.5. Someone has any idea? Regards. Sandro. -- View this message in context: http://www.nabble.com/rpart%3A-ID3-or-C4.5--tp19269303p19269303.html Sent from the R devel mailing list archive at Nabble.com.

HI, Dear R community, How to extract the variables actually used in tree construction? I want to extract these variables and combine other variable as my features in next step model building. > printcp(fit.dimer) Classification tree: rpart(formula = outcome ~ ., data = p_df, method = "class") Variables actually used in tree construction: [1] CT DP DY FC NE NW QT SK TA WC WD WG WW

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 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

My decision tree grows only with one split and based on what I see in E-Miner it should split on more variables. How can I adjust splitting criteria in R? Also is there way to indicate that some variables are binary, like variable Info_G is binary so in the results would be nice to see "2) Info_G=0" instead of "2) Info_G<0.5". Thank you in advance! And thanks for Eric who

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)

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

Hello everyone, I working in a public helath project and we have created a Decision Tree for categorical variables usign the package rpart. Our goal is to develop a model in order to predict presence/ausent of a diabetes and get a better understanding  of what are the important factors in a particular chilean population.  There are some importants variable that we have found.  Now we want to

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

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

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 using "plot" and "text" commands to display the decision tree I built, here is the code: plot(fit, compress=TRUE) text(fit, use.n=TRUE) but the the result of this code is not readable. Text doesn't get fully displayed (missing on the margines and overlapping in the middle). Where can I get info on how to adjust the tree display and text size display? Please help.

I''ve set xend service properties as follows: config/vnc-listen astring \''0.0.0.0\'' config/vncpasswd astring \''vnc\'' config/default-nic astring ''nge1'' but I get an authentication error whenever I try to connect to the console with ''vncviewer :0'' - I''m on the xvm host itself. other relevant data: SunOS

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]]

I am new in using R. I want to know all about building decision tree model in R. Few options which I searched are rpart and rattle to build a decision tree.Both the functions are giving me splits which are statistically appropriate. But I am not able to figure out how to change those splits as per my business requirement. for example : the automatic split of Age by using rattle is > 30 and

Hi , I have built decision tree using rpart . I want to do k Fold validation on the decision tree . Could you help how can i do that .. please tell the package which required for K fold validation. Regards, Santosh -- View this message in context: http://r.789695.n4.nabble.com/Need-Help-in-K-fold-validation-in-Decision-tree-tp4630730.html Sent from the R help mailing list archive at

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

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.

Ah yes I will have to use the predict function. But the predict function will not get me there really. If I can take the example that I have a model predicting whether or not I will play golf (this is the dependent value), and there are three independent variables Humidity(High, Medium, Low), Pending_Chores(Taxes, None, Laundry, Car Maintenance) and Wind (High, Low). I would like rules like