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Dorothea-test

Dorothea-test

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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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 This dataset is one of five datasets used in the NIPS 2003 feature selection challenge. Our website [Web Link] is still open for post-challenge submissions. Information about other related challenges are found at: [Web Link]. The CLOP package includes sample code to process these data: [Web Link]. All details about the preparation of the data are found in our technical report: Design of experiments for the NIPS 2003 variable selection benchmark, Isabelle Guyon, July 2003, [Web Link] (also included in the dataset archive). Such information was made available only after the end of the challenge. The data are split into training, validation, and test set. Target values are provided only for the 2 first sets. Test set performance results are obtained by submitting prediction results to: [Web Link]. The data are in the following format: dataname.param: Parameters and statistics about the data dataname.feat: Identities of the features (withheld, to avoid biasing feature selection). dataname_train.data: Training set (a sparse binary matrix, patterns in lines, features in columns: the number of the non-zero features are provided). dataname_valid.data: Validation set. dataname_test.data: Test set. dataname_train.labels: Labels (truth values of the classes) for training examples. dataname_valid.labels: Validation set labels (withheld during the benchmark, but provided now). dataname_test.labels: Test set labels (withheld, so the data can still be use as a benchmark). Attribute Information: We do not provide attribute information to avoid biasing feature selection. #autoxgboost #autoweka

100001 features

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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
a_13058numeric2 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
a_80130numeric2 unique values
0 missing
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0 missing
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0 missing
a_15106numeric2 unique values
0 missing
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0 missing
a_15362numeric2 unique values
0 missing
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0 missing
a_15618numeric2 unique values
0 missing
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0 missing
a_15874numeric2 unique values
0 missing
a_81410numeric2 unique values
0 missing
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0 missing
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0 missing
a_16386numeric2 unique values
0 missing
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0 missing
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0 missing
a_82178numeric2 unique values
0 missing
a_16898numeric1 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
a_83202numeric2 unique values
0 missing
a_17922numeric2 unique values
0 missing
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0 missing
a_18178numeric1 unique values
0 missing
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0 missing
a_18434numeric2 unique values
0 missing
a_83970numeric2 unique values
0 missing
a_18690numeric2 unique values
0 missing
a_84226numeric2 unique values
0 missing
a_18946numeric2 unique values
0 missing
a_84482numeric2 unique values
0 missing
a_19202numeric1 unique values
0 missing
a_84738numeric2 unique values
0 missing
a_19458numeric1 unique values
0 missing
a_84994numeric2 unique values
0 missing
a_19714numeric2 unique values
0 missing
a_85250numeric2 unique values
0 missing
a_19970numeric2 unique values
0 missing
a_85506numeric2 unique values
0 missing
a_20226numeric1 unique values
0 missing
a_85762numeric2 unique values
0 missing
a_20482numeric2 unique values
0 missing
a_86018numeric2 unique values
0 missing
a_20738numeric1 unique values
0 missing
a_86274numeric2 unique values
0 missing
a_20994numeric2 unique values
0 missing
a_86530numeric2 unique values
0 missing
a_21250numeric2 unique values
0 missing
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0 missing
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0 missing
a_87042numeric1 unique values
0 missing
a_21762numeric2 unique values
0 missing
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0 missing
a_22018numeric1 unique values
0 missing
a_87554numeric2 unique values
0 missing
a_22274numeric2 unique values
0 missing
a_87810numeric1 unique values
0 missing
a_22530numeric2 unique values
0 missing
a_88066numeric2 unique values
0 missing
a_22786numeric2 unique values
0 missing
a_88322numeric2 unique values
0 missing
a_23042numeric2 unique values
0 missing
a_88578numeric2 unique values
0 missing
a_23298numeric2 unique values
0 missing
a_88834numeric2 unique values
0 missing
a_23554numeric1 unique values
0 missing
a_89090numeric2 unique values
0 missing
a_23810numeric1 unique values
0 missing
a_89346numeric2 unique values
0 missing
a_24066numeric2 unique values
0 missing
a_89602numeric2 unique values
0 missing
a_24322numeric1 unique values
0 missing
a_89858numeric2 unique values
0 missing
a_24578numeric2 unique values
0 missing
a_90114numeric2 unique values
0 missing
a_24834numeric2 unique values
0 missing
a_90370numeric2 unique values
0 missing
a_25090numeric2 unique values
0 missing
a_90626numeric2 unique values
0 missing
a_25346numeric2 unique values
0 missing
a_90882numeric1 unique values
0 missing
a_25602numeric1 unique values
0 missing
a_91138numeric1 unique values
0 missing
a_25858numeric2 unique values
0 missing
a_91394numeric2 unique values
0 missing
a_26114numeric2 unique values
0 missing
a_91650numeric2 unique values
0 missing
a_26370numeric2 unique values
0 missing
a_91906numeric2 unique values
0 missing
a_26626numeric2 unique values
0 missing
a_92162numeric2 unique values
0 missing
a_26882numeric1 unique values
0 missing
a_92418numeric1 unique values
0 missing
a_27138numeric2 unique values
0 missing
a_92674numeric2 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing

62 properties

345
Number of instances (rows) of the dataset.
100001
Number of attributes (columns) of the dataset.
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.
100000
Number of numeric attributes.
1
Number of nominal attributes.
8.16
First quartile of skewness among attributes of the numeric type.
0.01
Mean of means among attributes of the numeric type.
0.05
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
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.
111.64
Second quartile (Median) of kurtosis among attributes of the numeric type.
289.86
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.01
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.
11.67
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
0.08
Mean standard deviation of attributes of the numeric type.
10.63
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.08
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.92
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
345
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
345
Third quartile of kurtosis among attributes of the numeric type.
0.27
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
100
Percentage of numeric attributes.
0.01
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
1.04
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
18.57
Third quartile of skewness among attributes of the numeric type.
18.57
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
64.97
First quartile of kurtosis among attributes of the numeric type.
0.11
Third quartile of standard deviation of attributes of the numeric type.
0.44
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
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.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
157.77
Mean kurtosis among attributes of the numeric type.

15 tasks

0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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