Data
clean2

clean2

active ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
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  • Chemistry derived Machine Learning
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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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f15numeric319 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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f25numeric304 unique values
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f26numeric379 unique values
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f27numeric338 unique values
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f28numeric272 unique values
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f29numeric278 unique values
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f30numeric341 unique values
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f31numeric276 unique values
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f34numeric419 unique values
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f38numeric324 unique values
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f42numeric329 unique values
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f43numeric407 unique values
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f44numeric328 unique values
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f45numeric340 unique values
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f46numeric392 unique values
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f47numeric265 unique values
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f48numeric411 unique values
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f49numeric381 unique values
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f50numeric374 unique values
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f51numeric390 unique values
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f52numeric329 unique values
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f53numeric333 unique values
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f54numeric389 unique values
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f55numeric313 unique values
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f56numeric415 unique values
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f57numeric363 unique values
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f58numeric271 unique values
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f60numeric412 unique values
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f62numeric305 unique values
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f63numeric205 unique values
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f67numeric181 unique values
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f68numeric287 unique values
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f69numeric314 unique values
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f70numeric287 unique values
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f76numeric113 unique values
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f78numeric392 unique values
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f81numeric397 unique values
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f82numeric294 unique values
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f84numeric361 unique values
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f86numeric320 unique values
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f98numeric354 unique values
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f99numeric181 unique values
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f100numeric331 unique values
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f101numeric255 unique values
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f102numeric255 unique values
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f103numeric396 unique values
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f104numeric337 unique values
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f105numeric277 unique values
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f106numeric382 unique values
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f107numeric319 unique values
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f108numeric388 unique values
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f109numeric328 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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f115numeric402 unique values
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f116numeric406 unique values
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f117numeric351 unique values
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f118numeric354 unique values
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f119numeric352 unique values
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f120numeric347 unique values
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f121numeric299 unique values
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f122numeric314 unique values
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f123numeric353 unique values
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f124numeric277 unique values
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f127numeric435 unique values
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f128numeric405 unique values
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f132numeric320 unique values
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f139numeric396 unique values
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f140numeric414 unique values
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f141numeric386 unique values
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f142numeric303 unique values
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f143numeric331 unique values
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f144numeric311 unique values
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f145numeric150 unique values
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f146numeric247 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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f150numeric329 unique values
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f151numeric197 unique values
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f152numeric184 unique values
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f153numeric151 unique values
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f154numeric257 unique values
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f155numeric323 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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f161numeric354 unique values
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f162numeric281 unique values
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f163numeric292 unique values
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f164numeric172 unique values
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f165numeric352 unique values
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f166numeric385 unique values
0 missing
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.
ClassEntropy
0.62
Entropy of the target attribute values.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Dimensionality
0.03
Number of attributes divided by the number of instances.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
Quartile2KurtosisOfNumericAtts
-0.52
Second quartile (Median) of kurtosis among attributes of the numeric type.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MeanSkewnessOfNumericAtts
0.47
Mean skewness among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-53.71
Second quartile (Median) of means among attributes of the numeric type.
MajorityClassPercentage
84.59
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
96.43
Mean standard deviation of attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassSize
5581
Number of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2SkewnessOfNumericAtts
0.09
Second quartile (Median) of skewness among attributes of the numeric type.
MaxAttributeEntropy
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-1.62
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
0.59
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
85.43
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxKurtosisOfNumericAtts
51.14
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-265.69
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
MaxMeansOfNumericAtts
3298.5
Maximum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
1.09
Third quartile of kurtosis among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
99.41
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
-1.11
Third quartile of means among attributes of the numeric type.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.44
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
0.59
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
MaxSkewnessOfNumericAtts
6.32
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
13.98
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
0.66
Third quartile of skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1904.82
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
15.41
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
-1.04
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
101.85
Third quartile of standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
1017
Number of instances belonging to the least frequent class.
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.
MeanKurtosisOfNumericAtts
1.68
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
MeanMeansOfNumericAtts
-25.47
Mean of means among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
-0.21
First quartile of skewness among attributes of the numeric type.
AutoCorrelation
1
Average class difference between consecutive instances.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
Quartile1StdDevOfNumericAtts
69.2
First quartile of standard deviation of attributes of the numeric type.
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 - 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
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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