Data
first-order-theorem-proving

first-order-theorem-proving

active ARFF Publicly available Visibility: public Uploaded 22-05-2015 by Rafael G. Mantovani
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • study_14 study_1 study_734 study_798 study_3297 study_7540 study_11168 study_422 study_705 study_1125 study_3900 study_5402 study_5736 study_3811 study_3858 study_4376 study_7432 study_666 study_3013 study_4332 study_6379 study_7540 study_12570 study_688 study_754 study_2056 study_3198 study_4051 study_4677 study_11301 study_1502 study_2599 study_3218 study_12848 study_13042 study_6884 study_10740 study_12848 study_2102 study_3419 study_5015 study_6786 study_7540 study_10375 study_11460 study_463 study_734 study_1651 study_1715 study_3218 study_11684 study_12397 study_2225 study_3007 study_3097 study_3419 study_4653 study_6835 study_13007 study_266 study_1952 study_3238 study_3504 study_7329 study_10930 study_11845 study_735 study_2982 study_5696 study_6998
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: James P Bridge, Sean B Holden and Lawrence C Paulson Source: UCI Please cite: James P Bridge, Sean B Holden and Lawrence C Paulson . Machine learning for first-order theorem proving: learning to select a good heuristic. Journal of Automated Reasoning, Springer 2012/13. Source: James P Bridge, Sean B Holden and Lawrence C Paulson University of Cambridge Computer Laboratory William Gates Building 15 JJ Thomson Avenue Cambridge CB3 0FD UK +44 (0)1223 763500 forename.surname '@' cl.cam.ac.uk Data Set Information: See the file dataset file. Attribute Information: The attributes are a mixture of static and dynamic features derived from theorems to be proved. See the paper for full details.

52 features

Class (target)nominal6 unique values
0 missing
V1numeric1309 unique values
0 missing
V2numeric1194 unique values
0 missing
V3numeric1148 unique values
0 missing
V4numeric1261 unique values
0 missing
V5numeric1394 unique values
0 missing
V6numeric1030 unique values
0 missing
V7numeric1309 unique values
0 missing
V8numeric49 unique values
0 missing
V9numeric1883 unique values
0 missing
V10numeric29 unique values
0 missing
V11numeric2431 unique values
0 missing
V12numeric219 unique values
0 missing
V13numeric3079 unique values
0 missing
V14numeric82 unique values
0 missing
V15numeric4674 unique values
0 missing
V16numeric3095 unique values
0 missing
V17numeric1969 unique values
0 missing
V18numeric134 unique values
0 missing
V19numeric3368 unique values
0 missing
V20numeric108 unique values
0 missing
V21numeric3841 unique values
0 missing
V22numeric103 unique values
0 missing
V23numeric4182 unique values
0 missing
V24numeric121 unique values
0 missing
V25numeric4393 unique values
0 missing
V26numeric803 unique values
0 missing
V27numeric4560 unique values
0 missing
V28numeric1023 unique values
0 missing
V29numeric4709 unique values
0 missing
V30numeric69 unique values
0 missing
V31numeric79 unique values
0 missing
V32numeric82 unique values
0 missing
V33numeric23 unique values
0 missing
V34numeric31 unique values
0 missing
V35numeric48 unique values
0 missing
V36numeric70 unique values
0 missing
V37numeric1053 unique values
0 missing
V38numeric1569 unique values
0 missing
V39numeric893 unique values
0 missing
V40numeric72 unique values
0 missing
V41numeric1041 unique values
0 missing
V42numeric37 unique values
0 missing
V43numeric29 unique values
0 missing
V44numeric1698 unique values
0 missing
V45numeric2246 unique values
0 missing
V46numeric1941 unique values
0 missing
V47numeric1676 unique values
0 missing
V48numeric1703 unique values
0 missing
V49numeric2315 unique values
0 missing
V50numeric1537 unique values
0 missing
V51numeric2462 unique values
0 missing

107 properties

6118
Number of instances (rows) of the dataset.
52
Number of attributes (columns) of the dataset.
6
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
51
Number of numeric attributes.
1
Number of nominal attributes.
228.19
Mean kurtosis among attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
10.45
Third quartile of skewness among attributes of the numeric type.
0.35
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
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0
Mean of means among attributes of the numeric type.
0.83
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.33
First quartile of kurtosis among attributes of the numeric type.
1
Third quartile of standard deviation of attributes of the numeric type.
0.71
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.47
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.01
First quartile of means among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.49
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
0.49
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.51
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.35
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
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
6
Average number of distinct values among the attributes of the nominal type.
1.33
First quartile of skewness among attributes of the numeric type.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.71
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.
0.47
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
7.44
Mean skewness among attributes of the numeric type.
0.97
First quartile of standard deviation of attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.49
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
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.99
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.51
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.35
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
0.46
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
41.75
Percentage of instances belonging to the most frequent class.
2554
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
15.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.3
Entropy of the target attribute values.
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Maximum entropy among attributes.
-0.97
Minimum kurtosis among attributes of the numeric type.
-0
Second quartile (Median) of means among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2944.65
Maximum kurtosis among attributes of the numeric type.
-0.04
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.51
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.58
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.05
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
3.14
Second quartile (Median) of skewness among attributes of the numeric type.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum mutual information between the nominal attributes and the target attribute.
6
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
6
The maximum number of distinct values among attributes of the nominal type.
-1.88
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.49
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
53.78
Maximum skewness among attributes of the numeric type.
0.84
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
137.49
Third quartile of kurtosis among attributes of the numeric type.
0.37
Average class difference between consecutive instances.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.51
Maximum standard deviation of attributes of the numeric type.
7.94
Percentage of instances belonging to the least frequent class.
98.08
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
0.71
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.47
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
486
Number of instances belonging to the least frequent class.
1.92
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.49
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
0.49
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

11 tasks

4 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
Define a new task