rpart

Does anyone know how to calculate the error rate for a decision tree with R? I am using the rpart() function. Assuming you mean computing error rate on the sample used to fit the model, you can use printcp() . For example, using the on-line example, > library(rpart) > fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) > printcp(fit) Classification tree: rpart(formula = Kyphosis ~ Age + Number + Start, data = kyphosis) Variables actually used in tree construction: [1] Age Start Root node error: 17/81 = 0.20988 n= 81 CP nsplit rel error xerror xstd 1 0.176471 0 1.00000 1.00000 0.21559

I would like to inspect all the observations that reached some node in an rpart decision tree. For example, in the following code: fit <- rpart(Kyphosis ~ Age + Start, data = kyphosis) fit n= 81 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 81 17 absent (0.79012346 0.20987654) 2) Start>=8.5 62 6 absent (0.90322581 0.09677419) 4) Start>=14.5 29 0 absent (1.00000000 0.00000000) * 5) Start< 14.5 33 6 absent (0.81818182 0.18181818) 10) Age< 55 12 0 absent (1.00000000 0.00000000) * 11) Age>=55 21 6 absent (0.71428571 0.28571429) 22) Age>=111 14 2 absent (0.85714286 0.14285714

I'm pretty new to R and I'm stuck with a pretty dumb problem. I'm calibrating a regression tree using the rpart package in order to do some classification and some forecasting. Thanks to R the calibration part is easy to do and easy to control. #the package rpart is needed library(rpart) # Loading of a big data file used for calibration my_data <- read.csv("my_file.csv", sep=",", header=TRUE) # Regression tree calibration tree <- rpart(Ratio ~ Attribute1 + Attribute2 + Attribute3 + Attribute4 + Attribute5, method="anova", data=my_data, control=rpart.control(minsplit=100, cp=0.0001)) After

I cannot understand what is going wrong here. data.train <- read.table("Assign2.WineComplete.csv",sep=",",header=T) # Building decision tree Train <- data.frame(residual.sugar=data.train$residual.sugar, total.sulfur.dioxide=data.train$total.sulfur.dioxide, alcohol=data.train$alcohol, quality=data.train$quality) Pre <- as.formula("pre ~ quality") fit <- rpart(Pre, method="class",data=Train) I am getting the following error : Error in eval(expr, envir, enclos) : object 'pre' not found Don't know why @Janos deleted his answer, but it's correct: your data frame Train doesn't have a column named

Let me start by saying that I have read many posts on Cross Validation and it seems there is much confusion out there. My understanding of that it is simply this: Perform k-fold Cross Validation i.e. 10 folds to understand the average error across the 10 folds. If acceptable then train the model on the complete data set. I am attempting to build a decision tree using rpart in R and taking advantage of the caret package. Below is the code I am using. # load libraries library(caret) library(rpart) # define training control train_control<- trainControl(method="cv", number=10) # train the model

As in my dataset ,the Leakage have two value 1,0. There are just about 300 rows with 1 and extra in 569378 rows are with 1. This would be the reason that I just got 1 root in the rpart result. How can I solve this? fm.pipe<-Leakage~PipeAge +PipePressure > printcp(CART.fit) Regression tree: rpart(formula = fm.pipe, data = Data) Variables actually used in tree construction: character(0) Root node error: 299.84/569378 = 0.00052661 n= 569378 CP nsplit rel error xerror xstd 1 0.0033246 0 1 0 0 There may not be a way to "solve" this, if the independent variables do not provide enough information to

I am using the caret package to train a model with "rpart" package; tr = train(y ~ ., data = trainingDATA, method = "rpart") Data has no missing values or NA's, but when running the command a warning message comes up; Warning message: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures. Does anyone know (or could point me to where to find an answer) what does this warning mean? I know it is telling me that there were missing values in resampled performance measures - but what does that exactly mean and how can a