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Author: Source: Unknown - Please cite: 1. Title: 1984 United States Congressional Voting Records Database 2. Source Information: (a) Source: Congressional Quarterly Almanac, 98th Congress, 2nd session 1984, Volume XL: Congressional Quarterly Inc. Washington, D.C., 1985. (b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) (c) Date: 27 April 1987 3. Past Usage - Publications 1. Schlimmer, J. C. (1987). Concept acquisition through representational adjustment. Doctoral dissertation, Department of Information and Computer Science, University of California, Irvine, CA. -- Results: about 90%-95% accuracy appears to be STAGGER's asymptote - Predicted attribute: party affiliation (2 classes) 4. Relevant Information: This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16 key votes identified by the CQA. The CQA lists nine different types of votes: voted for, paired for, and announced for (these three simplified to yea), voted against, paired against, and announced against (these three simplified to nay), voted present, voted present to avoid conflict of interest, and did not vote or otherwise make a position known (these three simplified to an unknown disposition). 5. Number of Instances: 435 (267 democrats, 168 republicans) 6. Number of Attributes: 16 + class name = 17 (all Boolean valued) 7. Attribute Information: 1. Class Name: 2 (democrat, republican) 2. handicapped-infants: 2 (y,n) 3. water-project-cost-sharing: 2 (y,n) 4. adoption-of-the-budget-resolution: 2 (y,n) 5. physician-fee-freeze: 2 (y,n) 6. el-salvador-aid: 2 (y,n) 7. religious-groups-in-schools: 2 (y,n) 8. anti-satellite-test-ban: 2 (y,n) 9. aid-to-nicaraguan-contras: 2 (y,n) 10. mx-missile: 2 (y,n) 11. immigration: 2 (y,n) 12. synfuels-corporation-cutback: 2 (y,n) 13. education-spending: 2 (y,n) 14. superfund-right-to-sue: 2 (y,n) 15. crime: 2 (y,n) 16. duty-free-exports: 2 (y,n) 17. export-administration-act-south-africa: 2 (y,n) 8. Missing Attribute Values: Denoted by "?" NOTE: It is important to recognize that "?" in this database does not mean that the value of the attribute is unknown. It means simply, that the value is not "yea" or "nay" (see "Relevant Information" section above). Attribute: #Missing Values: 1: 0 2: 0 3: 12 4: 48 5: 11 6: 11 7: 15 8: 11 9: 14 10: 15 11: 22 12: 7 13: 21 14: 31 15: 25 16: 17 17: 28 9. Class Distribution: (2 classes) 1. 45.2 percent are democrat 2. 54.8 percent are republican Class predictiveness and predictability: Pr(C|A=V) and Pr(A=V|C) Attribute 1: (A = handicapped-infants) 0.91; 1.21 (C=democrat; V=y) 0.09; 0.10 (C=republican; V=y) 0.43; 0.38 (C=democrat; V=n) 0.57; 0.41 (C=republican; V=n) 0.75; 0.03 (C=democrat; V=?) 0.25; 0.01 (C=republican; V=?) Attribute 2: (A = water-project-cost-sharing) 0.62; 0.45 (C=democrat; V=y) 0.38; 0.23 (C=republican; V=y) 0.62; 0.45 (C=democrat; V=n) 0.38; 0.23 (C=republican; V=n) 0.58; 0.10 (C=democrat; V=?) 0.42; 0.06 (C=republican; V=?) Attribute 3: (A = adoption-of-the-budget-resolution) 0.91; 0.87 (C=democrat; V=y) 0.09; 0.07 (C=republican; V=y) 0.17; 0.11 (C=democrat; V=n) 0.83; 0.44 (C=republican; V=n) 0.64; 0.03 (C=democrat; V=?) 0.36; 0.01 (C=republican; V=?) Attribute 4: (A = physician-fee-freeze) 0.08; 0.05 (C=democrat; V=y) 0.92; 0.50 (C=republican; V=y) 0.99; 0.92 (C=democrat; V=n) 0.01; 0.01 (C=republican; V=n) 0.73; 0.03 (C=democrat; V=?) 0.27; 0.01 (C=republican; V=?) Attribute 5: (A = el-salvador-aid) 0.26; 0.21 (C=democrat; V=y) 0.74; 0.48 (C=republican; V=y) 0.96; 0.75 (C=democrat; V=n) 0.04; 0.02 (C=republican; V=n) 0.80; 0.04 (C=democrat; V=?) 0.20; 0.01 (C=republican; V=?) Attribute 6: (A = religious-groups-in-schools) 0.45; 0.46 (C=democrat; V=y) 0.55; 0.46 (C=republican; V=y) 0.89; 0.51 (C=democrat; V=n) 0.11; 0.05 (C=republican; V=n) 0.82; 0.03 (C=democrat; V=?) 0.18; 0.01 (C=republican; V=?) Attribute 7: (A = anti-satellite-test-ban) 0.84; 0.75 (C=democrat; V=y) 0.16; 0.12 (C=republican; V=y) 0.32; 0.22 (C=democrat; V=n) 0.68; 0.38 (C=republican; V=n) 0.57; 0.03 (C=democrat; V=?) 0.43; 0.02 (C=republican; V=?) Attribute 8: (A = aid-to-nicaraguan-contras) 0.90; 0.82 (C=democrat; V=y) 0.10; 0.07 (C=republican; V=y) 0.25; 0.17 (C=democrat; V=n) 0.75; 0.41 (C=republican; V=n) 0.27; 0.01 (C=democrat; V=?) 0.73; 0.03 (C=republican; V=?) Attribute 9: (A = mx-missile) 0.91; 0.70 (C=democrat; V=y) 0.09; 0.06 (C=republican; V=y) 0.29; 0.22 (C=democrat; V=n) 0.71; 0.45 (C=republican; V=n) 0.86; 0.07 (C=democrat; V=?) 0.14; 0.01 (C=republican; V=?) Attribute 10: (A = immigration) 0.57; 0.46 (C=democrat; V=y) 0.43; 0.28 (C=republican; V=y) 0.66; 0.52 (C=democrat; V=n) 0.34; 0.23 (C=republican; V=n) 0.57; 0.01 (C=democrat; V=?) 0.43; 0.01 (C=republican; V=?) Attribute 11: (A = synfuels-corporation-cutback) 0.86; 0.48 (C=democrat; V=y) 0.14; 0.06 (C=republican; V=y) 0.48; 0.47 (C=democrat; V=n) 0.52; 0.43 (C=republican; V=n) 0.57; 0.04 (C=democrat; V=?) 0.43; 0.03 (C=republican; V=?) Attribute 12: (A = education-spending) 0.21; 0.13 (C=democrat; V=y) 0.79; 0.42 (C=republican; V=y) 0.91; 0.80 (C=democrat; V=n) 0.09; 0.06 (C=republican; V=n) 0.58; 0.07 (C=democrat; V=?) 0.42; 0.04 (C=republican; V=?) Attribute 13: (A = superfund-right-to-sue) 0.35; 0.27 (C=democrat; V=y) 0.65; 0.42 (C=republican; V=y) 0.89; 0.67 (C=democrat; V=n) 0.11; 0.07 (C=republican; V=n) 0.60; 0.06 (C=democrat; V=?) 0.40; 0.03 (C=republican; V=?) Attribute 14: (A = crime) 0.36; 0.34 (C=democrat; V=y) 0.64; 0.49 (C=republican; V=y) 0.98; 0.63 (C=democrat; V=n) 0.02; 0.01 (C=republican; V=n) 0.59; 0.04 (C=democrat; V=?) 0.41; 0.02 (C=republican; V=?) Attribute 15: (A = duty-free-exports) 0.92; 0.60 (C=democrat; V=y) 0.08; 0.04 (C=republican; V=y) 0.39; 0.34 (C=democrat; V=n) 0.61; 0.44 (C=republican; V=n) 0.57; 0.06 (C=democrat; V=?) 0.43; 0.04 (C=republican; V=?) Attribute 16: (A = export-administration-act-south-africa) 0.64; 0.65 (C=democrat; V=y) 0.36; 0.30 (C=republican; V=y) 0.19; 0.04 (C=democrat; V=n) 0.81; 0.15 (C=republican; V=n) 0.79; 0.31 (C=democrat; V=?) 0.21; 0.07 (C=republican; V=?)

17 features

Class (target)nominal2 unique values
0 missing
handicapped-infantsnominal2 unique values
12 missing
water-project-cost-sharingnominal2 unique values
48 missing
adoption-of-the-budget-resolutionnominal2 unique values
11 missing
physician-fee-freezenominal2 unique values
11 missing
el-salvador-aidnominal2 unique values
15 missing
religious-groups-in-schoolsnominal2 unique values
11 missing
anti-satellite-test-bannominal2 unique values
14 missing
aid-to-nicaraguan-contrasnominal2 unique values
15 missing
mx-missilenominal2 unique values
22 missing
immigrationnominal2 unique values
7 missing
synfuels-corporation-cutbacknominal2 unique values
21 missing
education-spendingnominal2 unique values
31 missing
superfund-right-to-suenominal2 unique values
25 missing
crimenominal2 unique values
17 missing
duty-free-exportsnominal2 unique values
28 missing
export-administration-act-south-africanominal2 unique values
104 missing

107 properties

435
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
392
Number of missing values in the dataset.
203
Number of instances with at least one value missing.
0
Number of numeric attributes.
17
Number of nominal attributes.
3.15
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
17
Number of binary attributes.
0.11
First quartile of mutual information between the nominal attributes and the target attribute.
0.05
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.9
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
0.85
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
2
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.97
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.05
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.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.05
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
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
0.99
Second quartile (Median) of entropy among attributes.
0.05
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.9
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
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
61.38
Percentage of instances belonging to the most frequent class.
0.93
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.96
Entropy of the target attribute values.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
267
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.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.21
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.05
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.04
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.91
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.
100
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Number of attributes divided by the number of instances.
0.72
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
46.67
Percentage of instances having missing values.
1
Third quartile of entropy among attributes.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
4.07
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
Minimum standard deviation of attributes of the numeric type.
5.3
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.58
Average class difference between consecutive instances.
0.85
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.
38.62
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.97
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
168
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.34
Third quartile of mutual information between the nominal attributes and the target attribute.
0.05
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
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.98
Average entropy of the attributes.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.97
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.9
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
0.85
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.
0.1
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.
0.97
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.24
Average mutual information between the nominal attributes and the target attribute.
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.05
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
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

12 tasks

939 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
380 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
367 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
218 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Class
212 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
90 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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