DEVELOPMENT... OpenML
Data
soybean

soybean

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jason
1 likes downloaded by 56 people , 66 total downloads 0 issues 0 downvotes
  • OpenML100 study_1 study_123 study_135 study_14 study_34 study_37 study_41 study_70 study_76 uci study_293
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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
lodgingnominal2 unique values
121 missing
stemnominal2 unique values
16 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
germinationnominal3 unique values
112 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
datenominal7 unique values
1 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

107 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.
0.95
Average class difference between consecutive instances.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.13
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.13
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.13
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
3.84
Entropy of the target attribute values.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.72
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.05
Number of attributes divided by the number of instances.
7.51
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
13.47
Percentage of instances belonging to the most frequent class.
92
Number of instances belonging to the most frequent class.
2.68
Maximum entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
1.29
Maximum mutual information between the nominal attributes and the target attribute.
19
The maximum number of distinct values among attributes of the nominal type.
Maximum skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
0.97
Average entropy of the attributes.
Mean kurtosis among attributes of the numeric type.
Mean of means among attributes of the numeric type.
0.51
Average mutual information between the nominal attributes and the target attribute.
0.89
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3.28
Average number of distinct values among the attributes of the nominal type.
Mean skewness among attributes of the numeric type.
Mean standard deviation of attributes of the numeric type.
0.07
Minimal entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0.05
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
1.17
Percentage of instances belonging to the least frequent class.
8
Number of instances belonging to the least frequent class.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.09
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
16
Number of binary attributes.
44.44
Percentage of binary attributes.
17.72
Percentage of instances having missing values.
9.5
Percentage of missing values.
0
Percentage of numeric attributes.
100
Percentage of nominal attributes.
0.46
First quartile of entropy among attributes.
First quartile of kurtosis among attributes of the numeric type.
First quartile of means among attributes of the numeric type.
0.26
First quartile of mutual information between the nominal attributes and the target attribute.
First quartile of skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.92
Second quartile (Median) of entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.46
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.41
Third quartile of entropy among attributes.
Third quartile of kurtosis among attributes of the numeric type.
Third quartile of means among attributes of the numeric type.
0.72
Third quartile of mutual information between the nominal attributes and the target attribute.
Third quartile of skewness among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.27
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.27
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
2.88
Standard deviation of the number of distinct values among attributes of the nominal type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.12
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

106 tasks

16132 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
321 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
320 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
178 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 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
318 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
182 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: average_cost - target_feature: class
0 runs - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
1312 runs - target_feature: class
1311 runs - target_feature: class
1310 runs - target_feature: class
1310 runs - target_feature: class
1310 runs - target_feature: class
1310 runs - target_feature: class
1310 runs - target_feature: class
1309 runs - target_feature: class
1309 runs - target_feature: class
1308 runs - target_feature: class
1307 runs - target_feature: class
1307 runs - target_feature: class
1307 runs - target_feature: class
1306 runs - target_feature: class
1304 runs - target_feature: class
1304 runs - target_feature: class
1304 runs - target_feature: class
1301 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
Define a new task