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first-order-theorem-proving

first-order-theorem-proving

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Author: James P Bridge, Sean B Holden and Lawrence C Paulson Source: [UCI](https://archive.ics.uci.edu/ml/datasets/First-order+theorem+proving) 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

62 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.
0
Percentage of binary attributes.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.97
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
2944.65
Maximum kurtosis among attributes of the numeric type.
-0.04
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
137.49
Third quartile of kurtosis among attributes of the numeric type.
0.05
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
98.08
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
6
The minimal number of distinct values among attributes of the nominal type.
1.92
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
6
The maximum number of distinct values among attributes of the nominal type.
-1.88
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
10.45
Third quartile of skewness among attributes of the numeric type.
53.78
Maximum skewness among attributes of the numeric type.
0.84
Minimum standard deviation of attributes of the numeric type.
1.33
First quartile of kurtosis among attributes of the numeric type.
1
Third quartile of standard deviation of attributes of the numeric type.
1.51
Maximum standard deviation of attributes of the numeric type.
7.94
Percentage of instances belonging to the least frequent class.
-0.01
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
486
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
228.19
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
1.33
First quartile of skewness among attributes of the numeric type.
-0
Mean of means among attributes of the numeric type.
0.97
First quartile of standard deviation of attributes of the numeric type.
0.37
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
2.3
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
15.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
6
Average number of distinct values among the attributes of the nominal type.
-0
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
7.44
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
41.75
Percentage of instances belonging to the most frequent class.
0.99
Mean standard deviation of attributes of the numeric type.
3.14
Second quartile (Median) of skewness among attributes of the numeric type.
2554
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

32 tasks

12312 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
43 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - target_feature: Class
1312 runs - target_feature: Class
1309 runs - target_feature: Class
1304 runs - target_feature: Class
1303 runs - target_feature: Class
1300 runs - target_feature: Class
1299 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
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