Data
soybean

soybean

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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  • OpenML100 study_1 study_123 study_135 study_14 study_34 study_37 study_41 study_70 study_76 uci
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Author: R.S. Michalski and R.L. Chilausky (Donors: Ming Tan & Jeff Schlimmer) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Soybean+(Large)) - 1988 Please cite: 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. Large Soybean Database This is the large soybean database from the UCI repository, with its training and test database combined into a single file. 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. An unknown value is encoded as "?''. ### Attribute Information 1. date: april,may,june,july,august,september,october,?. 2. plant-stand: normal,lt-normal,?. 3. precip: lt-norm,norm,gt-norm,?. 4. temp: lt-norm,norm,gt-norm,?. 5. hail: yes,no,?. 6. crop-hist: diff-lst-year,same-lst-yr,same-lst-two-yrs, same-lst-sev-yrs,?. 7. area-damaged: scattered,low-areas,upper-areas,whole-field,?. 8. severity: minor,pot-severe,severe,?. 9. seed-tmt: none,fungicide,other,?. 10. germination: 90-100%,80-89%,lt-80%,?. 11. plant-growth: norm,abnorm,?. 12. leaves: norm,abnorm. 13. leafspots-halo: absent,yellow-halos,no-yellow-halos,?. 14. leafspots-marg: w-s-marg,no-w-s-marg,dna,?. 15. leafspot-size: lt-1/8,gt-1/8,dna,?. 16. leaf-shread: absent,present,?. 17. leaf-malf: absent,present,?. 18. leaf-mild: absent,upper-surf,lower-surf,?. 19. stem: norm,abnorm,?. 20. lodging: yes,no,?. 21. stem-cankers: absent,below-soil,above-soil,above-sec-nde,?. 22. canker-lesion: dna,brown,dk-brown-blk,tan,?. 23. fruiting-bodies: absent,present,?. 24. external decay: absent,firm-and-dry,watery,?. 25. mycelium: absent,present,?. 26. int-discolor: none,brown,black,?. 27. sclerotia: absent,present,?. 28. fruit-pods: norm,diseased,few-present,dna,?. 29. fruit spots: absent,colored,brown-w/blk-specks,distort,dna,?. 30. seed: norm,abnorm,?. 31. mold-growth: absent,present,?. 32. seed-discolor: absent,present,?. 33. seed-size: norm,lt-norm,?. 34. shriveling: absent,present,?. 35. roots: norm,rotted,galls-cysts,?. ### Classes -- 19 Classes = {diaporthe-stem-canker, charcoal-rot, rhizoctonia-root-rot, phytophthora-rot, brown-stem-rot, powdery-mildew, downy-mildew, brown-spot, bacterial-blight, bacterial-pustule, purple-seed-stain, anthracnose, phyllosticta-leaf-spot, alternarialeaf-spot, frog-eye-leaf-spot, diaporthe-pod-&-stem-blight, cyst-nematode, 2-4-d-injury, herbicide-injury} ### Revelant papers 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. 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.

36 features

class (target)nominal19 unique values
0 missing
datenominal7 unique values
1 missing
plant-standnominal2 unique values
36 missing
precipnominal3 unique values
38 missing
tempnominal3 unique values
30 missing
hailnominal2 unique values
121 missing
crop-histnominal4 unique values
16 missing
area-damagednominal4 unique values
1 missing
severitynominal3 unique values
121 missing
seed-tmtnominal3 unique values
121 missing
germinationnominal3 unique values
112 missing
plant-growthnominal2 unique values
16 missing
leavesnominal2 unique values
0 missing
leafspots-halonominal3 unique values
84 missing
leafspots-margnominal3 unique values
84 missing
leafspot-sizenominal3 unique values
84 missing
leaf-shreadnominal2 unique values
100 missing
leaf-malfnominal2 unique values
84 missing
leaf-mildnominal3 unique values
108 missing
stemnominal2 unique values
16 missing
lodgingnominal2 unique values
121 missing
stem-cankersnominal4 unique values
38 missing
canker-lesionnominal4 unique values
38 missing
fruiting-bodiesnominal2 unique values
106 missing
external-decaynominal3 unique values
38 missing
myceliumnominal2 unique values
38 missing
int-discolornominal3 unique values
38 missing
sclerotianominal2 unique values
38 missing
fruit-podsnominal4 unique values
84 missing
fruit-spotsnominal4 unique values
106 missing
seednominal2 unique values
92 missing
mold-growthnominal2 unique values
92 missing
seed-discolornominal2 unique values
106 missing
seed-sizenominal2 unique values
92 missing
shrivelingnominal2 unique values
106 missing
rootsnominal3 unique values
31 missing

19 properties

683
Number of instances (rows) of the dataset.
36
Number of attributes (columns) of the dataset.
19
Number of distinct values of the target attribute (if it is nominal).
2337
Number of missing values in the dataset.
121
Number of instances with at least one value missing.
0
Number of numeric attributes.
36
Number of nominal attributes.
16
Number of binary attributes.
44.44
Percentage of binary attributes.
17.72
Percentage of instances having missing values.
0.95
Average class difference between consecutive instances.
9.5
Percentage of missing values.
0.05
Number of attributes divided by the number of instances.
0
Percentage of numeric attributes.
13.47
Percentage of instances belonging to the most frequent class.
100
Percentage of nominal attributes.
92
Number of instances belonging to the most frequent class.
1.17
Percentage of instances belonging to the least frequent class.
8
Number of instances belonging to the least frequent class.

91 tasks

8820 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
323 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
321 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
179 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
48 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
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