Soybean {mlbench}R Documentation

Soybean Database

Description

There are 19 classes, only the first 15 of which have been used in prior work. The folklore seems to be that the last four classes are unjustified by the data since they have so few examples. There are 35 categorical attributes, some nominal and some ordered. The value ``dna'' means does not apply. The values for attributes are encoded numerically, with the first value encoded as ``0,'' the second as ``1,'' and so forth.

Usage

data(Soybean)

Format

A data frame with 683 observations on 36 variables. There are 35 categorical attributes, all numerical and a nominal denoting the class.
[,1] Class the 19 classes
[,2] date apr(0),may(1),june(2),july(3),aug(4),sept(5),oct(6).
[,3] plant.stand normal(0),lt-normal(1).
[,4] precip lt-norm(0),norm(1),gt-norm(2).
[,5] temp lt-norm(0),norm(1),gt-norm(2).
[,6] hail yes(0),no(1).
[,7] crop.hist dif-lst-yr(0),s-l-y(1),s-l-2-y(2), s-l-7-y(3).
[,8] area.dam scatter(0),low-area(1),upper-ar(2),whole-field(3).
[,9] sever minor(0),pot-severe(1),severe(2).
[,10] seed.tmt none(0),fungicide(1),other(2).
[,11] germ 90-100%(0),80-89%(1),lt-80%(2).
[,12] plant.growth norm(0),abnorm(1).
[,13] leaves norm(0),abnorm(1).
[,14] leaf.halo absent(0),yellow-halos(1),no-yellow-halos(2).
[,15] leaf.marg w-s-marg(0),no-w-s-marg(1),dna(2).
[,16] leaf.size lt-1/8(0),gt-1/8(1),dna(2).
[,17] leaf.shread absent(0),present(1).
[,18] leaf.malf absent(0),present(1).
[,19] leaf.mild absent(0),upper-surf(1),lower-surf(2).
[,20] stem norm(0),abnorm(1).
[,21] lodging yes(0),no(1).
[,22] stem.cankers absent(0),below-soil(1),above-s(2),ab-sec-nde(3).
[,23] canker.lesion dna(0),brown(1),dk-brown-blk(2),tan(3).
[,24] fruiting.bodies absent(0),present(1).
[,25] ext.decay absent(0),firm-and-dry(1),watery(2).
[,26] mycelium absent(0),present(1).
[,27] int.discolor none(0),brown(1),black(2).
[,28] sclerotia absent(0),present(1).
[,29] fruit.pods norm(0),diseased(1),few-present(2),dna(3).
[,30] fruit.spots absent(0),col(1),br-w/blk-speck(2),distort(3),dna(4).
[,31] seed norm(0),abnorm(1).
[,32] mold.growth absent(0),present(1).
[,33] seed.discolor absent(0),present(1).
[,34] seed.size norm(0),lt-norm(1).
[,35] shriveling absent(0),present(1).
[,36] roots norm(0),rotted(1),galls-cysts(2).

Source

These data have been taken from the UCI Repository Of Machine Learning Databases at

and were converted to R format by Evgenia.Dimitriadou@ci.tuwien.ac.at.

References

Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning (pp. 121-134). Ann Arbor, Michigan: Morgan Kaufmann. – IWN recorded a 97.1% classification accuracy – 290 training and 340 test instances

Fisher,D.H. & Schlimmer,J.C. (1988). Concept Simplification and Predictive Accuracy. Proceedings of the Fifth International Conference on Machine Learning (pp. 22-28). Ann Arbor, Michigan: Morgan Kaufmann. – Notes why this database is highly predictable


[Package mlbench version 1.0-0 Index]