Decision Tree: Am I Missing Anything?
Not very sure what the problem is as I was not able to take your data for run. You might want to use dput() command to present the data. Now on the programming side. As we can see that we have more than 2 levels for the brands and hence method = class is not able to able to understand what you actually want from it. Suggestion : For predictions having more than 2 levels I will go for Weka and specifically C4.5 algorithm. You also have the RWeka package for it. Best Regards, Bhupendrasinh Thakre Sent from my iPhone
On Sep 20, 2012, at 9:47 PM, Vik Rubenfeld <vikr at mindspring.com> wrote:
I'm working with some data from which a client would like to make a decision tree predicting brand preference based on inputs such as price, speed, etc. After running the decision tree analysis using rpart, it appears that this data is not capable of predicting brand preference. Here's the data set: BRND PRI PROM FORM FAMI DRRE FREC MODE SPED REVW Brand 1 0.6989 0.4731 0.7849 0.6989 0.7419 0.6022 0.8817 0.9032 0.6452 Brand 2 0.8621 0.3793 0.8621 0.931 0.7586 0.6897 0.8966 0.9655 0.8276 Brand 3 0.6 0.1 0.6 0.7 0.9 0.7 0.7 0.8 0.6 Brand 4 0.6429 0.25 0.5714 0.5 0.6071 0.5 0.75 0.8214 0.5 Brand 5 0.7586 0.4224 0.7328 0.6638 0.7328 0.6379 0.8621 0.8621 0.6897 Brand 6 0.75 0.0833 0.5833 0.4167 0.5 0.4167 0.75 0.6667 0.5 Brand 7 0.7742 0.4839 0.6129 0.5161 0.8065 0.6452 0.7742 0.9032 0.6129 Brand 8 0.6429 0.2679 0.6964 0.7143 0.875 0.5536 0.8036 0.9464 0.6607 Brand 9 0.575 0.175 0.65 0.55 0.625 0.375 0.825 0.85 0.475 Brand 10 0.8095 0.5238 0.6667 0.6429 0.6667 0.5952 0.8571 0.8095 0.5714 Brand 11 0.6308 0.3 0.6077 0.5846 0.6769 0.5231 0.7462 0.8846 0.6 Brand 12 0.7212 0.3152 0.7152 0.6545 0.6606 0.503 0.8061 0.8909 0.6 Brand 13 0.7419 0.2258 0.6129 0.5806 0.7097 0.6129 0.871 0.9677 0.3226 Brand 14 0.7176 0.2706 0.6353 0.5647 0.6941 0.4471 0.7176 0.9412 0.5176 Brand 15 0.7287 0.3437 0.5995 0.5788 0.8527 0.5478 0.8217 0.8941 0.6227 Brand 16 0.7 0.4 0.6 0.4 1 0.4 0.9 0.9 0.5 Brand 17 0.7193 0.3333 0.6667 0.6667 0.7018 0.5263 0.7719 0.8596 0.7018 Brand 18 0.7778 0.4127 0.6508 0.6349 0.7937 0.6032 0.8571 0.9206 0.619 Brand 19 0.8028 0.2817 0.6197 0.4366 0.7042 0.4366 0.7183 0.9155 0.5634 Brand 20 0.7736 0.2453 0.6226 0.3774 0.5849 0.3019 0.717 0.8679 0.4717 Brand 21 0.8481 0.2152 0.6329 0.4051 0.6329 0.4557 0.6962 0.8481 0.3418 Brand 22 0.75 0.3333 0.6667 0.5 0.6667 0.5833 0.9167 0.9167 0.4167 Here are my R commands:
test.df = read.csv("test.csv")
head(test.df)
BRND PRI PROM FORM FAMI DRRE FREC MODE SPED REVW 1 Brand 1 0.6989 0.4731 0.7849 0.6989 0.7419 0.6022 0.8817 0.9032 0.6452 2 Brand 2 0.8621 0.3793 0.8621 0.9310 0.7586 0.6897 0.8966 0.9655 0.8276 3 Brand 3 0.6000 0.1000 0.6000 0.7000 0.9000 0.7000 0.7000 0.8000 0.6000 4 Brand 4 0.6429 0.2500 0.5714 0.5000 0.6071 0.5000 0.7500 0.8214 0.5000 5 Brand 5 0.7586 0.4224 0.7328 0.6638 0.7328 0.6379 0.8621 0.8621 0.6897 6 Brand 6 0.7500 0.0833 0.5833 0.4167 0.5000 0.4167 0.7500 0.6667 0.5000
testTree = rpart(BRAND~PRI + PROM + FORM + FAMI+ DRRE + FREC + MODE + SPED + REVW, method="class", data=test.df)
printcp(testTree)
Classification tree:
rpart(formula = BRND ~ PRI + PROM + FORM + FAMI + DRRE + FREC +
MODE + SPED + REVW, data = test.df, method = "class")
Variables actually used in tree construction:
[1] FORM
Root node error: 21/22 = 0.95455
n= 22
CP nsplit rel error xerror xstd
1 0.047619 0 1.00000 1.0476 0
2 0.010000 1 0.95238 1.0476 0
I note that only one variable (FORM) was actually used in tree construction. When I run a plot using:
plot(testTree) text(testTree)
...I get a tree with one branch. It looks to me like I'm doing everything right, and this data is just not capable of predicting brand preference. Am I missing anything? Thanks very much in advance for any thoughts! -Vik [[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.