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Dorothea

Dorothea

active Sparse_ARFF Publicly available Visibility: public Uploaded 08-11-2015 by unknown
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DOROTHEA is a drug discovery dataset. Chemical compounds represented by structural molecular features must be classified as active (binding to thrombin) or inactive. This is one of 5 datasets of the NIPS 2003 feature selection challenge. Source: a. Original owners The dataset with which DOROTHEA was created is one of the KDD (Knowledge Discovery in Data Mining) Cup 2001. The original dataset and papers of the winners of the competition are available at: http://www.cs.wisc.edu/~dpage/kddcup2001/. DuPont Pharmaceuticals graciously provided this data set for the KDD Cup 2001 competition. All publications referring to analysis of this data set should acknowledge DuPont Pharmaceuticals Research Laboratories and KDD Cup 2001. b. Donor of database This version of the database was prepared for the NIPS 2003 variable and feature selection benchmark by Isabelle Guyon, 955 Creston Road, Berkeley, CA 94708, USA (isabelle '@' clopinet.com). Data Set Information: Drugs are typically small organic molecules that achieve their desired activity by binding to a target site on a receptor. The first step in the discovery of a new drug is usually to identify and isolate the receptor to which it should bind, followed by testing many small molecules for their ability to bind to the target site. This leaves researchers with the task of determining what separates the active (binding) compounds from the inactive (non-binding) ones. Such a determination can then be used in the design of new compounds that not only bind, but also have all the other properties required for a drug (solubility, oral absorption, lack of side effects, appropriate duration of action, toxicity, etc.). The original data were modified for the purpose of the feature selection challenge. In particular, we added a number of distractor feature called 'probes' having no predictive power. The order of the features and patterns were randomized. DOROTHEA -- Positive ex. -- Negative ex. -- Total Training set -- 78 -- 722 -- 800 Validation set -- 34 -- 316 -- 350 Test set -- 78 -- 722 -- 800 All -- 190 -- 1760 -- 1950 We mapped Active compounds to the target value +1 (positive examples) and Inactive compounds to the target value –1 (negative examples). Number of variables/features/attributes: Real: 50000 Probes: 50000 Total: 100000

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V99987numeric2 unique values
0 missing
V99988numeric2 unique values
0 missing
V99989numeric2 unique values
0 missing
V99990numeric2 unique values
0 missing
V99991numeric2 unique values
0 missing
V99992numeric1 unique values
0 missing
V99993numeric2 unique values
0 missing
V99994numeric1 unique values
0 missing
V99995numeric2 unique values
0 missing
V99996numeric2 unique values
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V99997numeric2 unique values
0 missing
V99998numeric2 unique values
0 missing
V99999numeric2 unique values
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V84861numeric2 unique values
0 missing
V84006numeric2 unique values
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V84007numeric2 unique values
0 missing
V84008numeric2 unique values
0 missing
V84009numeric1 unique values
0 missing
V84010numeric2 unique values
0 missing
V84011numeric1 unique values
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V84012numeric2 unique values
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V84013numeric2 unique values
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V84014numeric2 unique values
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V84015numeric2 unique values
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V84016numeric2 unique values
0 missing
V84017numeric2 unique values
0 missing
V84018numeric2 unique values
0 missing
V84019numeric1 unique values
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V84020numeric2 unique values
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V84021numeric2 unique values
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V84036numeric2 unique values
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V84037numeric2 unique values
0 missing
V84038numeric2 unique values
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V84039numeric2 unique values
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V84040numeric2 unique values
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V84041numeric2 unique values
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V84042numeric2 unique values
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V84043numeric2 unique values
0 missing
V84044numeric2 unique values
0 missing
V84045numeric2 unique values
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V84046numeric2 unique values
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V84047numeric2 unique values
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V84048numeric2 unique values
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V84049numeric2 unique values
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V84062numeric2 unique values
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V84063numeric2 unique values
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V84064numeric2 unique values
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V84065numeric2 unique values
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V84066numeric2 unique values
0 missing
V84067numeric1 unique values
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V84068numeric2 unique values
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V84070numeric2 unique values
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V84071numeric2 unique values
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V84075numeric2 unique values
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V84076numeric2 unique values
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V84077numeric2 unique values
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V84079numeric2 unique values
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V84080numeric2 unique values
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V84081numeric2 unique values
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V84082numeric2 unique values
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V84085numeric2 unique values
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V84086numeric2 unique values
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V84087numeric2 unique values
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V84088numeric2 unique values
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V84089numeric1 unique values
0 missing
V84090numeric1 unique values
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V84091numeric2 unique values
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V84118numeric1 unique values
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V84136numeric2 unique values
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V84138numeric1 unique values
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V84140numeric1 unique values
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V84205numeric1 unique values
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V84247numeric1 unique values
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V84255numeric2 unique values
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V84256numeric2 unique values
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V84257numeric1 unique values
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V84298numeric1 unique values
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V84342numeric1 unique values
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V84406numeric2 unique values
0 missing
V84407numeric1 unique values
0 missing
Show first 100 features

62 properties

NumberOfInstances
1150
Number of instances (rows) of the dataset.
NumberOfFeatures
100001
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
100000
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
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
72
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0
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
8.59
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.04
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
159.99
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.01
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
12.72
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
379.99
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.01
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
19.53
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.11
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.82
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
0.01
Mean of means among attributes of the numeric type.
ClassEntropy
0.46
Entropy of the target attribute values.
Dimensionality
86.96
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
90.26
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1038
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1150
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.27
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
33.91
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.44
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
315.09
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
15.38
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.08
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.9
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
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
1.05
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
9.74
Percentage of instances belonging to the least frequent class.
MinorityClassSize
112
Number of instances belonging to the least frequent class.
Show all 62 properties

21 tasks

Supervised Classification on Dorothea
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Learning Curve on Dorothea
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Dorothea
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Dorothea
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Dorothea
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Dorothea
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Dorothea
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Dorothea
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Dorothea
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on Dorothea
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on Dorothea
0 runs
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
Clustering on Dorothea
0 runs - estimation_procedure: 50 times Clustering
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