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
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