rpart

I used caret to train an rpart model below. trainIndex <- createDataPartition(d$Happiness, p=.8, list=FALSE) dtrain <- d[trainIndex, ] dtest <- d[-trainIndex, ] fitControl <- trainControl(## 10-fold CV method = "repeatedcv", number=10, repeats=10) fitRpart <- train(Happiness ~ ., data=dtrain, method="rpart", trControl = fitControl) testRpart <- predict(fitRpart, newdata=dtest) dtest contains 1296 observations, so I expected testRpart to produce a vector of length 1296. Instead it's 1077 long, i.e. 219 short. When I ran the prediction on the first 220 rows of dtest , I got a predicted result of

I am using the prp function from the rpart.plot package to plot a tree. For categorical data like states, it gives a really long list of variables and makes it less readable. Is there any way to wrap text to two or more lines if exceeds some length? Here's an example that wraps long split labels over multiple lines. The maximum length of each line is 25 characters. Change the 25 to suit your purposes. (This example is derived from Section 6.1 in the rpart.plot vignette .) tree <- rpart(Price/1000 ~ Mileage + Type + Country, cu.summary) split.fun <- function(x, labs, digits, varlen, faclen) { #

i am currently using the rpart package to fit a regression tree to a data with relatively few observations and several thousand categorical predictors taking two possible values. from testing the package out on smaller data i know that in this instance it doesn't matter whether i declare the regressors as categorical (i.e. factors) or leave them as they are (they are coded as +/-1). however, i would still like to understand why passing my explanatory variables as factors significantly slows the algorithm down (not least because i shall soon get new data where response takes 3 diffirent values

Is it possible to extract the node assignment for a fitted rpart tree? What about when I apply the model to new data? The idea is that I would like to use the nodes as a way to cluster my data. In other packages (e.g. SPSS), I can save the predicted class, probabilities, and node number for further analysis. Given how powerful R can be, I imagine there is a simple solution to this. topepo Try using the partykit package: library(rpart) z.auto <- rpart(Mileage ~ Weight, car.test.frame) library(partykit) z.auto2 <- as.party(z.auto) predict(z.auto2, car.test.frame[1:3,], type = "node") # Eagle

I have constructed a decision tree using rpart for a dataset. I have then divided the data into 2 parts - a training dataset and a test dataset. A tree has been constructed for the dataset using the training data. I want to calculate the accuracy of the predictions based on the model that was created. My code is shown below: library(rpart) #reading the data data = read.table("source") names(data) <- c("a", "b", "c", "d", "class") #generating test and train data - Data selected randomly with a 80/20 split trainIndex <- sample(1:nrow(x), 0.8 * nrow(x)) train <- data[trainIndex,] test <- data[

I'm taking part in the Coursera Practical Machine Learning course, and the coursework requires building predictive models using this dataset . After splitting the data into training and testing datasets, based on the outcome of interest (herewith labelled y , but is in fact the classe variable in the dataset): inTrain <- createDataPartition(y = data$y, p = 0.75, list = F) training <- data[inTrain, ] testing <- data[-inTrain, ] I have tried 2 different methods: modFit <- caret::train(y ~ ., method = "rpart", data = training) pred <- predict(modFit, newdata = testing) confusionMatrix(pred,

I want to test in a programmatically way one rule generated from a tree. In the trees the path between the root and a leaf (terminal node) could be interpreted as a rule. In R, we could use the rpart package and do the following: (In this post, I will use the iris data set, for example purposes only) library(rpart) model <- rpart(Species ~ ., data=iris) With this two lines I got a tree named model , whose class is rpart.object ( rpart documentation, page 21). This object has a lot of information, and supports a variety of methods. In particular, the object has a frame variable (which can be