covsample {DAAGxtras} | R Documentation |
Forest cover type is recorded, for every 50th observation taken from 581012 observations in the original dataset, together with a physical geographical variables that may account for the forest cover type.
data(covsample)
A data frame with 11318 observations on the following 55 variables.
V1
V2
V3
V4
V5
V6
V7
V8
V9
V10
V11
V12
V13
V14
V15
V16
V17
V18
V19
V20
V21
V22
V23
V24
V25
V26
V27
V28
V29
V30
V31
V32
V33
V34
V35
V36
V37
V38
V39
V40
V41
V42
V43
V44
V45
V46
V47
V48
V49
V50
V51
V52
V53
V54
V55
For details, see http://kdd.ics.uci.edu/databases/covertype/covertype.data.html
For detailed information on the UCI dataset, see http://kdd.ics.uci.edu/databases/covertype/covertype.data.html
Variables V1
to V54
are physical geographical
variables. Variable V55
is cover type, one of types 1 - 7.
Note the omission of any information on geographical location. Distance through the data seems however to be, in part, a proxy for geographical location.
http://kdd.ics.uci.edu/databases/covertype/covertype.html
Blackard, Jock A. 1998. "Comparison of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types." Ph.D. dissertation. Department of Forest Sciences. Colorado State University. Fort Collins, Colorado.
data(covsample) options(digits=3) tab.sample <- table(covsample$V55) tab.sample/sum(tab.sample) rm(covsample) data(covtrain) tab.train <- table(covtrain$V55) tab.train/sum(tab.train) rm(covtrain) data(covtest) tab.test <- table(covtest$V55) tab.test/sum(tab.test) rm(covtest)