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Gisette-test

Gisette-test

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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Source: a. Original owners The data set was constructed from the MNIST data that is made available by Yann LeCun and Corinna Cortes at http://yann.lecun.com/exdb/mnist/. 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 digits have been size-normalized and centered in a fixed-size image of dimension 28x28. The original data were modified for the purpose of the feature selection challenge. In particular, pixels were samples at random in the middle top part of the feature containing the information necessary to disambiguate 4 from 9 and higher order features were created as products of these pixels to plunge the problem in a higher dimensional feature space. We also added a number of distractor features called 'probes' having no predictive power. The order of the features and patterns were randomized. GISETTE -- Positive ex. -- Negative ex. -- Total Training set -- 3000 -- 3000 -- 6000 Validation set -- 500 -- 500 -- 1000 Test set -- 3250 -- 3250 -- 6500 All -- 6750 -- 6750 -- 13500 Number of variables/features/attributes: Real: 2500 Probes: 2500 Total: 5000 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 coma delimited regular matrix, patterns in lines, features in columns). 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). #autoxgboost #autoweka

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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_2606numeric281 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
a_3886numeric3 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
a_1583numeric123 unique values
0 missing
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0 missing
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0 missing
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0 missing
a_2607numeric39 unique values
0 missing
a_2863numeric119 unique values
0 missing
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0 missing
a_3375numeric76 unique values
0 missing
a_3631numeric1 unique values
0 missing
a_3887numeric105 unique values
0 missing
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0 missing
a_4399numeric1 unique values
0 missing
a_4655numeric194 unique values
0 missing
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0 missing
a_48numeric99 unique values
0 missing
a_304numeric229 unique values
0 missing
a_560numeric157 unique values
0 missing
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0 missing
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0 missing
a_1328numeric220 unique values
0 missing
a_1584numeric133 unique values
0 missing
a_1840numeric44 unique values
0 missing
a_2096numeric245 unique values
0 missing
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0 missing
a_2608numeric1 unique values
0 missing
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0 missing
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0 missing
a_3376numeric175 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
a_4656numeric154 unique values
0 missing
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0 missing
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0 missing
a_305numeric224 unique values
0 missing
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0 missing
a_817numeric88 unique values
0 missing
a_1073numeric213 unique values
0 missing
a_1329numeric1 unique values
0 missing
a_1585numeric224 unique values
0 missing
a_1841numeric10 unique values
0 missing
a_2097numeric1 unique values
0 missing
a_2353numeric69 unique values
0 missing
a_2609numeric14 unique values
0 missing
a_2865numeric299 unique values
0 missing
a_3121numeric15 unique values
0 missing
a_3377numeric290 unique values
0 missing
a_3633numeric238 unique values
0 missing
a_3889numeric3 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

2100
Number of instances (rows) of the dataset.
5001
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.
5000
Number of numeric attributes.
1
Number of nominal attributes.
1.65
First quartile of skewness among attributes of the numeric type.
104.72
Mean of means among attributes of the numeric type.
91.71
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.
9.7
Second quartile (Median) of kurtosis among attributes of the numeric type.
2.38
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
59.51
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.
7.28
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.
213.65
Mean standard deviation of attributes of the numeric type.
3.34
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.02
Percentage of binary attributes.
211.45
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.9
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
2100
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
62.71
Third quartile of kurtosis among attributes of the numeric type.
797.18
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.98
Percentage of numeric attributes.
161.62
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.
0.02
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.
-1.66
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
7.86
Third quartile of skewness among attributes of the numeric type.
45.83
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0.96
First quartile of kurtosis among attributes of the numeric type.
332.3
Third quartile of standard deviation of attributes of the numeric type.
462.53
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.
10.88
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.
152.61
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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