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Dexter-test

Dexter-test

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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Source: a. Original owners The original data set we used is a subset of the well-known Reuters text categorization benchmark. The data was originally collected and labeled by Carnegie Group, Inc. and Reuters, Ltd. in the course of developing the CONSTRUE text categorization system. It is hosted by the UCI KDD repository: http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html. David D. Lewis is hosting valuable resources about this data (see http://www.daviddlewis.com/resources/testcollections/reuters21578/). We used the “corporate acquisition” text classification class pre-processed by Thorsten Joachims <thorsten>. The data is one of the examples of the software package SVM-Light., see http://svmlight.joachims.org/. The example can be downloaded from ftp://ftp-ai.cs.uni-dortmund.de/pub/Users/thorsten/svm_light/examples/example1.tar.gz. 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: The original data were formatted by Thorsten Joachims in the “bag-of-words” representation. There were 9947 features (of which 2562 are always zeros for all the examples) representing frequencies of occurrence of word stems in text. The task is to learn which Reuters articles are about 'corporate acquisitions'. We added a number of distractor feature called 'probes' having no predictive power. The order of the features and patterns were randomized. DEXTER -- Positive ex. -- Negative ex. -- Total Training set --150 -- 150 -- 300 Validation set -- 150 -- 150 -- 300 Test set -- 1000 -- 1000 -- 2000 All -- 1300 -- 1300 -- 2600 Number of variables/features/attributes: Real: 9947 Probes: 10053 Total: 20000 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 matrix, patterns in lines, features in columns: feature number followed by value). 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 feature information to avoid biasing feature selection. #autoxgboost #autoweka

20001 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
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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_3851numeric1 unique values
0 missing
a_4107numeric1 unique values
0 missing
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0 missing
a_4619numeric1 unique values
0 missing
a_4875numeric1 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_6667numeric2 unique values
0 missing
a_6923numeric2 unique values
0 missing
a_7179numeric1 unique values
0 missing
a_7435numeric1 unique values
0 missing
a_7691numeric1 unique values
0 missing
a_7947numeric4 unique values
0 missing
a_8203numeric1 unique values
0 missing
a_8459numeric2 unique values
0 missing
a_8715numeric1 unique values
0 missing
a_8971numeric2 unique values
0 missing
a_9227numeric1 unique values
0 missing
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0 missing
a_9739numeric3 unique values
0 missing
a_9995numeric1 unique values
0 missing
a_10251numeric1 unique values
0 missing
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0 missing
a_10763numeric1 unique values
0 missing
a_11019numeric2 unique values
0 missing
a_11275numeric1 unique values
0 missing
a_11531numeric1 unique values
0 missing
a_11787numeric2 unique values
0 missing
a_12043numeric2 unique values
0 missing
a_12299numeric1 unique values
0 missing
a_12555numeric4 unique values
0 missing
a_12811numeric1 unique values
0 missing
a_13067numeric1 unique values
0 missing
a_13323numeric1 unique values
0 missing
a_13579numeric1 unique values
0 missing
a_13835numeric1 unique values
0 missing
a_14091numeric1 unique values
0 missing
a_14347numeric1 unique values
0 missing
a_14603numeric1 unique values
0 missing
a_14859numeric1 unique values
0 missing
a_15115numeric1 unique values
0 missing
a_15371numeric1 unique values
0 missing
a_15627numeric1 unique values
0 missing
a_15883numeric2 unique values
0 missing
a_16139numeric1 unique values
0 missing
a_16395numeric1 unique values
0 missing
a_16651numeric1 unique values
0 missing
a_16907numeric1 unique values
0 missing
a_17163numeric2 unique values
0 missing
a_17419numeric2 unique values
0 missing
a_17675numeric1 unique values
0 missing
a_17931numeric1 unique values
0 missing
a_18187numeric1 unique values
0 missing
a_18443numeric3 unique values
0 missing
a_18699numeric5 unique values
0 missing
a_18955numeric1 unique values
0 missing
a_19211numeric1 unique values
0 missing
a_19467numeric1 unique values
0 missing
a_19723numeric1 unique values
0 missing
a_19979numeric1 unique values
0 missing
a_12numeric1 unique values
0 missing
a_268numeric4 unique values
0 missing
a_524numeric3 unique values
0 missing
a_780numeric3 unique values
0 missing
a_1036numeric2 unique values
0 missing
a_1292numeric2 unique values
0 missing
a_1548numeric10 unique values
0 missing
a_1804numeric1 unique values
0 missing
a_2060numeric1 unique values
0 missing
a_2316numeric3 unique values
0 missing
a_2572numeric1 unique values
0 missing
a_2828numeric2 unique values
0 missing
a_3084numeric1 unique values
0 missing
a_3340numeric1 unique values
0 missing
a_3596numeric1 unique values
0 missing
a_3852numeric1 unique values
0 missing
a_4108numeric1 unique values
0 missing
a_4364numeric1 unique values
0 missing
a_4620numeric1 unique values
0 missing
a_4876numeric2 unique values
0 missing
a_5132numeric1 unique values
0 missing
a_5388numeric1 unique values
0 missing
a_5644numeric1 unique values
0 missing
a_5900numeric1 unique values
0 missing
a_6156numeric1 unique values
0 missing
a_6412numeric1 unique values
0 missing
a_6668numeric1 unique values
0 missing
a_6924numeric2 unique values
0 missing
a_7180numeric1 unique values
0 missing
a_7436numeric1 unique values
0 missing
a_7692numeric1 unique values
0 missing
a_7948numeric1 unique values
0 missing
a_8204numeric1 unique values
0 missing
a_8460numeric1 unique values
0 missing
a_8716numeric1 unique values
0 missing
a_8972numeric1 unique values
0 missing
a_9228numeric1 unique values
0 missing
a_9484numeric2 unique values
0 missing
a_9740numeric2 unique values
0 missing
a_9996numeric2 unique values
0 missing
a_10252numeric1 unique values
0 missing
a_10508numeric1 unique values
0 missing
a_10764numeric1 unique values
0 missing
a_11020numeric1 unique values
0 missing
a_11276numeric2 unique values
0 missing
a_11532numeric2 unique values
0 missing
a_11788numeric1 unique values
0 missing
a_12044numeric2 unique values
0 missing
a_12300numeric1 unique values
0 missing
a_12556numeric1 unique values
0 missing
a_12812numeric1 unique values
0 missing
a_13068numeric2 unique values
0 missing

62 properties

180
Number of instances (rows) of the dataset.
20001
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.
20000
Number of numeric attributes.
1
Number of nominal attributes.
9.64
First quartile of skewness among attributes of the numeric type.
0.48
Mean of means among attributes of the numeric type.
0
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.
180
Second quartile (Median) of kurtosis among attributes of the numeric type.
111.12
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0
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.
11.52
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.
3.46
Mean standard deviation of attributes of the numeric type.
13.42
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
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.65
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
180
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
180
Third quartile of kurtosis among attributes of the numeric type.
326.68
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.
0.34
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.
0.32
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
13.42
Third quartile of skewness among attributes of the numeric type.
13.42
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
94.28
First quartile of kurtosis among attributes of the numeric type.
4.47
Third quartile of standard deviation of attributes of the numeric type.
165.1
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.
141.78
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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