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clean2

clean2

active ARFF public Visibility: public Uploaded 06-04-2017 by Arnold
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Derived from the Musk dataset: https://www.openml.org/d/1116

169 features

class (target)nominal2 unique values
0 missing
molecule_namenumeric102 unique values
0 missing
conformation_namenumeric6598 unique values
0 missing
f1numeric202 unique values
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f2numeric260 unique values
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f3numeric221 unique values
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f4numeric257 unique values
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f5numeric129 unique values
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f6numeric358 unique values
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f7numeric323 unique values
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f8numeric389 unique values
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f9numeric347 unique values
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f10numeric399 unique values
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f11numeric387 unique values
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f12numeric384 unique values
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f13numeric397 unique values
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f14numeric329 unique values
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f16numeric327 unique values
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f17numeric350 unique values
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f18numeric375 unique values
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f19numeric398 unique values
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f20numeric388 unique values
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f21numeric419 unique values
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f22numeric304 unique values
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f23numeric397 unique values
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f24numeric368 unique values
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f26numeric379 unique values
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f110numeric268 unique values
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f111numeric292 unique values
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f112numeric406 unique values
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f113numeric368 unique values
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f114numeric337 unique values
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f147numeric144 unique values
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f148numeric294 unique values
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f149numeric295 unique values
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f151numeric197 unique values
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f152numeric184 unique values
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f154numeric257 unique values
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f156numeric286 unique values
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f157numeric258 unique values
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f158numeric398 unique values
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f159numeric386 unique values
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f160numeric278 unique values
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f162numeric281 unique values
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f164numeric172 unique values
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Show first 100 features

62 properties

NumberOfInstances
6598
Number of instances (rows) of the dataset.
NumberOfFeatures
169
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
168
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0.59
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.41
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.59
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.04
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-86.99
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
-0.21
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
69.2
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.52
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-53.71
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.09
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
85.43
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.09
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
-1.11
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.66
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
101.85
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
1
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
-25.47
Mean of means among attributes of the numeric type.
ClassEntropy
0.62
Entropy of the target attribute values.
Dimensionality
0.03
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
84.59
Percentage of instances belonging to the most frequent class.
MajorityClassSize
5581
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
51.14
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
3298.5
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
6.32
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1904.82
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
1.68
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.47
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
96.43
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.62
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-265.69
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.44
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
13.98
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
15.41
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1017
Number of instances belonging to the least frequent class.
Show all 62 properties

22 tasks

Supervised Classification on clean2
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on clean2
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Learning Curve on clean2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on clean2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on clean2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on clean2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on clean2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on clean2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on clean2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on clean2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on clean2
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
Clustering on clean2
0 runs
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
Clustering on clean2
0 runs - estimation_procedure: 50 times Clustering
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