2
$\begingroup$

I have built the CART model, however I want to understand how we predict/validate the results with Validation data.

d=sort(sample(nrow(red_data),nrow(red_data)*.7))

#Selecting training sample
train_data=red_data[d,]
test_data=red_data[-d,]
nrow(train_data) #20133#
nrow(test_data)#8629#

rpart(formula = red_status ~ VALUE_CLASS + GAP_1 + cs_rr1_2, 
    data = train_data, method = "class", control = rpart.control(minsplit = 6))

print(cfit)
summary(cfit)

prp(cfit,type=2, extra=106, nn=TRUE,fallen.leaves=TRUE)

This is what I have done.

Could some one help me on this

$\endgroup$
  • 1
    $\begingroup$ You should provide some data so that people can reproduce what you've done. Also, you could consider adding some other tags, as you've only got the R tag right now, and no statistical ones. $\endgroup$ – Patrick Coulombe Mar 6 '14 at 5:08
1
$\begingroup$

Since we don't have access to your data, I am guessing that pages 39- 40 of the manual will help you. I'll use the iris data set since it's readily available (and in the documentation):

data(iris)
d <- sort(sample(nrow(iris),nrow(iris)*.7))
(iris.rpart <- rpart(Species ~ . , data = iris, subset = d, method = "class"))
# n= 105 
# 
# node), split, n, loss, yval, (yprob)
# * denotes terminal node
# 
# 1) root 105 68 setosa (0.3523810 0.3333333 0.3142857)  
# 2) Petal.Length< 2.45 37  0 setosa (1.0000000 0.0000000 0.0000000) *
#   3) Petal.Length>=2.45 68 33 versicolor (0.0000000 0.5147059 0.4852941)  
# 6) Petal.Width< 1.75 40  5 versicolor (0.0000000 0.8750000 0.1250000) *
#   7) Petal.Width>=1.75 28  0 virginica (0.0000000 0.0000000 1.0000000) *

# this validation of your test data set (30% of records) shows that the 
# model predicted 44 of 45 correct.  Not bad.
table(predict(iris.rpart, iris[-d,], type = "class"), iris[-d, "Species"])
# setosa versicolor virginica
# setosa         15          0         0
# versicolor      0         15         1
# virginica       0          0        14

?predict.rpart

This could be a comment but. . .

In all seriousness, probably the best help anyone can give you is "read the manual." Also I could point you to the rattle package: this is a pretty simple point and click interface to the rpart package, along with several other modeling tools.

By the way: as noted on the 2nd page of the manual, CART is a trademarked name. "Recursive Partitioning" is a generic name, so you should probably describe your model accordingly.

I hope this helps.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.