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mlbench (version 2.1-5)

Soybean: 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", package = "mlbench")

Arguments

Format

A data frame with 683 observations on 36 variables. There are 35 categorical attributes, all numerical and a nominal denoting the class.

[,1]Classthe 19 classes
[,2]dateapr(0),may(1),june(2),july(3),aug(4),sept(5),oct(6).
[,3]plant.standnormal(0),lt-normal(1).
[,4]preciplt-norm(0),norm(1),gt-norm(2).
[,5]templt-norm(0),norm(1),gt-norm(2).
[,6]hailyes(0),no(1).
[,7]crop.histdif-lst-yr(0),s-l-y(1),s-l-2-y(2), s-l-7-y(3).
[,8]area.damscatter(0),low-area(1),upper-ar(2),whole-field(3).
[,9]severminor(0),pot-severe(1),severe(2).
[,10]seed.tmtnone(0),fungicide(1),other(2).
[,11]germ90-100%(0),80-89%(1),lt-80%(2).
[,12]plant.growthnorm(0),abnorm(1).
[,13]leavesnorm(0),abnorm(1).
[,14]leaf.haloabsent(0),yellow-halos(1),no-yellow-halos(2).
[,15]leaf.margw-s-marg(0),no-w-s-marg(1),dna(2).
[,16]leaf.sizelt-1/8(0),gt-1/8(1),dna(2).
[,17]leaf.shreadabsent(0),present(1).
[,18]leaf.malfabsent(0),present(1).
[,19]leaf.mildabsent(0),upper-surf(1),lower-surf(2).
[,20]stemnorm(0),abnorm(1).
[,21]lodgingyes(0),no(1).
[,22]stem.cankersabsent(0),below-soil(1),above-s(2),ab-sec-nde(3).
[,23]canker.lesiondna(0),brown(1),dk-brown-blk(2),tan(3).
[,24]fruiting.bodiesabsent(0),present(1).
[,25]ext.decayabsent(0),firm-and-dry(1),watery(2).
[,26]myceliumabsent(0),present(1).
[,27]int.discolornone(0),brown(1),black(2).
[,28]sclerotiaabsent(0),present(1).
[,29]fruit.podsnorm(0),diseased(1),few-present(2),dna(3).
[,30]fruit.spotsabsent(0),col(1),br-w/blk-speck(2),distort(3),dna(4).
[,31]seednorm(0),abnorm(1).
[,32]mold.growthabsent(0),present(1).
[,33]seed.discolorabsent(0),present(1).
[,34]seed.sizenorm(0),lt-norm(1).
[,35]shrivelingabsent(0),present(1).
[,36]rootsnorm(0),rotted(1),galls-cysts(2).

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

Blake, C.L. & Merz, C.J. (1998). UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Irvine, Department of Information and Computer Science. Formerly available from http://www.ics.uci.edu/~mlearn/MLRepository.html.

Examples

Run this code
data("Soybean", package = "mlbench")
summary(Soybean)

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