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

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

92 tasks

15860 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
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182 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
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