Description Usage Format Details Source References Examples

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.

1 |

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

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.

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DAAGxtras documentation built on May 29, 2017, 1:33 p.m.

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