covsample: Sample of UCI Machine Learning Forest Cover Dataset

In DAAGxtras: Data Sets and Functions, supplementary to DAAG

Description

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.

Usage

data(covsample)

Format

A data frame with 11318 observations on the following 55 variables.

V1

a numeric vector

V2

a numeric vector

V3

a numeric vector

V4

a numeric vector

V5

a numeric vector

V6

a numeric vector

V7

a numeric vector

V8

a numeric vector

V9

a numeric vector

V10

a numeric vector

V11

a numeric vector

V12

a numeric vector

V13

a numeric vector

V14

a numeric vector

V15

a numeric vector

V16

a numeric vector

V17

a numeric vector

V18

a numeric vector

V19

a numeric vector

V20

a numeric vector

V21

a numeric vector

V22

a numeric vector

V23

a numeric vector

V24

a numeric vector

V25

a numeric vector

V26

a numeric vector

V27

a numeric vector

V28

a numeric vector

V29

a numeric vector

V30

a numeric vector

V31

a numeric vector

V32

a numeric vector

V33

a numeric vector

V34

a numeric vector

V35

a numeric vector

V36

a numeric vector

V37

a numeric vector

V38

a numeric vector

V39

a numeric vector

V40

a numeric vector

V41

a numeric vector

V42

a numeric vector

V43

a numeric vector

V44

a numeric vector

V45

a numeric vector

V46

a numeric vector

V47

a numeric vector

V48

a numeric vector

V49

a numeric vector

V50

a numeric vector

V51

a numeric vector

V52

a numeric vector

V53

a numeric vector

V54

a numeric vector

V55

a numeric vector

For details, see http://kdd.ics.uci.edu/databases/covertype/covertype.data.html

Details

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.

Source

http://kdd.ics.uci.edu/databases/covertype/covertype.html

References

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.

Examples

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)

DAAGxtras documentation built on May 1, 2019, 10:18 p.m.