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

- Source: R.S. Michalski and R.L. Chilausky "Learning by Being Told and Learning from Examples: An Experimental Comparison of the Two Methods of Knowledge Acquisition in the Context of Developing an Expert System for Soybean Disease Diagnosis", International Journal of Policy Analysis and Information Systems, Vol. 4, No. 2, 1980.
- Donor: Ming Tan & Jeff Schlimmer (Jeff.Schlimmer%cs.cmu.edu)

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

- ftp://ftp.ics.uci.edu/pub/machine-learning-databases
- http://www.ics.uci.edu/~mlearn/MLRepository.html

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

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