Data
Dorothea

Dorothea

active Sparse_ARFF Publicly available Visibility: public Uploaded 08-11-2015 by Gabriel Prat
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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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V24321numeric2 unique values
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V89857numeric2 unique values
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V24577numeric2 unique values
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V90113numeric2 unique values
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V24833numeric2 unique values
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V90369numeric2 unique values
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V25089numeric2 unique values
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V90625numeric2 unique values
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V25345numeric2 unique values
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V90881numeric2 unique values
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V25601numeric2 unique values
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V91137numeric2 unique values
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V25857numeric2 unique values
0 missing
V91393numeric1 unique values
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V26113numeric2 unique values
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V91649numeric2 unique values
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V91905numeric2 unique values
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V26625numeric2 unique values
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V92161numeric2 unique values
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V26881numeric2 unique values
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V92417numeric2 unique values
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V27137numeric2 unique values
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V92673numeric2 unique values
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V28161numeric2 unique values
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V93953numeric2 unique values
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V28673numeric2 unique values
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V94465numeric2 unique values
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V29185numeric2 unique values
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V94721numeric2 unique values
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V29441numeric2 unique values
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V94977numeric2 unique values
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V29697numeric2 unique values
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V95233numeric1 unique values
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V29953numeric2 unique values
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V95489numeric2 unique values
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V30209numeric2 unique values
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V95745numeric1 unique values
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V30465numeric2 unique values
0 missing
V96001numeric2 unique values
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V30721numeric2 unique values
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V96257numeric2 unique values
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V30977numeric2 unique values
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V96513numeric2 unique values
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V31233numeric2 unique values
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V96769numeric2 unique values
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V31489numeric2 unique values
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V97025numeric2 unique values
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V31745numeric2 unique values
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V97281numeric2 unique values
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V32001numeric2 unique values
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V97537numeric2 unique values
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V97793numeric2 unique values
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V32513numeric2 unique values
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V98049numeric2 unique values
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V98305numeric2 unique values
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V33025numeric2 unique values
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V98561numeric2 unique values
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V33281numeric2 unique values
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V98817numeric2 unique values
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V33537numeric2 unique values
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V99073numeric2 unique values
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V33793numeric2 unique values
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V99329numeric2 unique values
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V34049numeric2 unique values
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V99585numeric2 unique values
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V34305numeric2 unique values
0 missing
V99841numeric2 unique values
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V34561numeric2 unique values
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V34817numeric2 unique values
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V35073numeric2 unique values
0 missing
V35329numeric2 unique values
0 missing
V35585numeric2 unique values
0 missing
V35841numeric2 unique values
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V36097numeric2 unique values
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V36353numeric2 unique values
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V36609numeric2 unique values
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V36865numeric2 unique values
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V37121numeric2 unique values
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V37377numeric2 unique values
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V37633numeric2 unique values
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V37889numeric2 unique values
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V38145numeric2 unique values
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V38657numeric2 unique values
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V39169numeric2 unique values
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V39681numeric2 unique values
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V47105numeric2 unique values
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V47361numeric1 unique values
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V47873numeric2 unique values
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V48129numeric2 unique values
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V48385numeric2 unique values
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V48641numeric2 unique values
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V49409numeric2 unique values
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V61185numeric2 unique values
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V62209numeric2 unique values
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V64513numeric2 unique values
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V64769numeric2 unique values
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V65281numeric2 unique values
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V2numeric2 unique values
0 missing
V65538numeric2 unique values
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V258numeric2 unique values
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V65794numeric2 unique values
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V514numeric2 unique values
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V66050numeric2 unique values
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V770numeric2 unique values
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V66306numeric2 unique values
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V1026numeric2 unique values
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V66562numeric2 unique values
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V1282numeric2 unique values
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V66818numeric2 unique values
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V1538numeric2 unique values
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V1794numeric2 unique values
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V2306numeric2 unique values
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V3842numeric2 unique values
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V5890numeric2 unique values
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V71426numeric2 unique values
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V6146numeric2 unique values
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V71682numeric2 unique values
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V6402numeric2 unique values
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V71938numeric2 unique values
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V6658numeric2 unique values
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V72194numeric2 unique values
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V6914numeric2 unique values
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V72450numeric2 unique values
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V7170numeric2 unique values
0 missing
V72706numeric2 unique values
0 missing
V7426numeric2 unique values
0 missing
V72962numeric2 unique values
0 missing
V7682numeric2 unique values
0 missing
V73218numeric2 unique values
0 missing
V7938numeric2 unique values
0 missing
V73474numeric2 unique values
0 missing
V8194numeric2 unique values
0 missing
V73730numeric2 unique values
0 missing
V8450numeric2 unique values
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V73986numeric2 unique values
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V8706numeric2 unique values
0 missing
V74242numeric2 unique values
0 missing
V8962numeric2 unique values
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V74498numeric2 unique values
0 missing
V9218numeric2 unique values
0 missing
V74754numeric2 unique values
0 missing
V9474numeric2 unique values
0 missing
V75010numeric2 unique values
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V9730numeric2 unique values
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V75522numeric2 unique values
0 missing
V10242numeric2 unique values
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V75778numeric2 unique values
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V10498numeric2 unique values
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V76034numeric2 unique values
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V10754numeric2 unique values
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V76290numeric2 unique values
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V11010numeric2 unique values
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V11266numeric2 unique values
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V76802numeric2 unique values
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V11522numeric2 unique values
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V77058numeric2 unique values
0 missing
V11778numeric2 unique values
0 missing
V77314numeric2 unique values
0 missing
V12034numeric2 unique values
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V77570numeric2 unique values
0 missing
V12290numeric2 unique values
0 missing
V77826numeric2 unique values
0 missing
V12546numeric2 unique values
0 missing
V78082numeric2 unique values
0 missing
V12802numeric2 unique values
0 missing
V78338numeric2 unique values
0 missing
V13058numeric2 unique values
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0 missing
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0 missing
V13570numeric2 unique values
0 missing
V79106numeric2 unique values
0 missing
V13826numeric2 unique values
0 missing
V79362numeric2 unique values
0 missing
V14082numeric2 unique values
0 missing
V79618numeric2 unique values
0 missing
V14338numeric2 unique values
0 missing
V79874numeric2 unique values
0 missing
V14594numeric2 unique values
0 missing
V80130numeric2 unique values
0 missing
V14850numeric2 unique values
0 missing
V80386numeric2 unique values
0 missing
V15106numeric2 unique values
0 missing
V80642numeric2 unique values
0 missing
V15362numeric2 unique values
0 missing
V80898numeric2 unique values
0 missing
V15618numeric2 unique values
0 missing
V81154numeric2 unique values
0 missing
V15874numeric2 unique values
0 missing
V81410numeric2 unique values
0 missing
V16130numeric2 unique values
0 missing
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0 missing
V16386numeric2 unique values
0 missing
V81922numeric2 unique values
0 missing
V16642numeric2 unique values
0 missing
V82178numeric2 unique values
0 missing
V16898numeric2 unique values
0 missing
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0 missing
V17154numeric2 unique values
0 missing
V82690numeric2 unique values
0 missing
V17410numeric2 unique values
0 missing
V82946numeric2 unique values
0 missing
V17666numeric2 unique values
0 missing
V83202numeric2 unique values
0 missing
V17922numeric2 unique values
0 missing
V83458numeric2 unique values
0 missing
V18178numeric2 unique values
0 missing
V83714numeric2 unique values
0 missing
V18434numeric2 unique values
0 missing
V83970numeric2 unique values
0 missing
V18690numeric2 unique values
0 missing
V84226numeric2 unique values
0 missing
V18946numeric2 unique values
0 missing
V84482numeric2 unique values
0 missing
V19202numeric1 unique values
0 missing
V84738numeric2 unique values
0 missing
V19458numeric2 unique values
0 missing
V84994numeric2 unique values
0 missing
V19714numeric2 unique values
0 missing
V85250numeric2 unique values
0 missing
V19970numeric2 unique values
0 missing
V85506numeric2 unique values
0 missing
V20226numeric2 unique values
0 missing
V85762numeric2 unique values
0 missing
V20482numeric2 unique values
0 missing
V86018numeric2 unique values
0 missing
V20738numeric2 unique values
0 missing
V86274numeric2 unique values
0 missing
V20994numeric2 unique values
0 missing
V86530numeric2 unique values
0 missing
V21250numeric2 unique values
0 missing
V86786numeric2 unique values
0 missing
V21506numeric2 unique values
0 missing
V87042numeric1 unique values
0 missing
V21762numeric2 unique values
0 missing
V87298numeric2 unique values
0 missing
V22018numeric2 unique values
0 missing
V87554numeric2 unique values
0 missing
V22274numeric2 unique values
0 missing
V87810numeric2 unique values
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V22530numeric2 unique values
0 missing
V88066numeric2 unique values
0 missing
V22786numeric2 unique values
0 missing
V88322numeric2 unique values
0 missing
V23042numeric2 unique values
0 missing
V88578numeric2 unique values
0 missing
V23298numeric2 unique values
0 missing
V88834numeric2 unique values
0 missing
V23554numeric2 unique values
0 missing
V89090numeric2 unique values
0 missing
V23810numeric2 unique values
0 missing
V89346numeric2 unique values
0 missing
V24066numeric2 unique values
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V89602numeric2 unique values
0 missing
V24322numeric2 unique values
0 missing
V89858numeric2 unique values
0 missing
V24578numeric2 unique values
0 missing
V90114numeric2 unique values
0 missing
V24834numeric2 unique values
0 missing
V90370numeric2 unique values
0 missing
V25090numeric2 unique values
0 missing
V90626numeric2 unique values
0 missing
V25346numeric2 unique values
0 missing
V90882numeric2 unique values
0 missing
V25602numeric2 unique values
0 missing
V91138numeric1 unique values
0 missing
V25858numeric2 unique values
0 missing
V91394numeric2 unique values
0 missing
V26114numeric2 unique values
0 missing
V91650numeric2 unique values
0 missing
V26370numeric2 unique values
0 missing
V91906numeric2 unique values
0 missing
V26626numeric2 unique values
0 missing
V92162numeric2 unique values
0 missing
V26882numeric2 unique values
0 missing
V92418numeric1 unique values
0 missing
V27138numeric2 unique values
0 missing
V92674numeric2 unique values
0 missing
V27394numeric2 unique values
0 missing
V92930numeric2 unique values
0 missing
V27650numeric2 unique values
0 missing

62 properties

1150
Number of instances (rows) of the dataset.
100001
Number of attributes (columns) of the dataset.
2
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.
0
Percentage of binary attributes.
0.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.9
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1150
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
379.99
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.
1.05
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
19.53
Third quartile of skewness among attributes of the numeric type.
33.91
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
72
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.
9.74
Percentage 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.
112
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
315.09
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
8.59
First quartile of skewness among attributes of the numeric type.
0.01
Mean of means among attributes of the numeric type.
0.04
First quartile of standard deviation of attributes of the numeric type.
0.82
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.46
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.
159.99
Second quartile (Median) of kurtosis among attributes of the numeric type.
86.96
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.
15.38
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
90.26
Percentage of instances belonging to the most frequent class.
0.08
Mean standard deviation of attributes of the numeric type.
12.72
Second quartile (Median) of skewness among attributes of the numeric type.
1038
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

8 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - 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: 50 times Clustering
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