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Madelon-test

Madelon-test

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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Source: Isabelle Guyon Clopinet 955 Creston Road Berkeley, CA 90708 isabelle '@' clopinet.com Data Set Information: MADELON is an artificial dataset containing data points grouped in 32 clusters placed on the vertices of a five dimensional hypercube and randomly labeled +1 or -1. The five dimensions constitute 5 informative features. 15 linear combinations of those features were added to form a set of 20 (redundant) informative features. Based on those 20 features one must separate the examples into the 2 classes (corresponding to the +-1 labels). We added a number of distractor feature called 'probes' having no predictive power. The order of the features and patterns were randomized. MADELON -- Positive ex. -- Negative ex. -- Total Training set -- 1000 -- 1000 -- 2000 Validation set -- 300 -- 300 -- 600 Test set -- 900 -- 900 -- 1800 All -- 2200 -- 2200 -- 4400 Number of variables/features/attributes: Real: 20 Probes: 480 Total: 500 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 (in the order the features are found in the data). dataname_train.data: Training set (a space-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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62 properties

780
Number of instances (rows) of the dataset.
501
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.
500
Number of numeric attributes.
1
Number of nominal attributes.
-0.04
First quartile of skewness among attributes of the numeric type.
488.06
Mean of means among attributes of the numeric type.
11.72
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.
0.09
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.64
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
485.13
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.
0.03
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.
24.77
Mean standard deviation of attributes of the numeric type.
0.03
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.2
Percentage of binary attributes.
23.22
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.99
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1.1
Maximum kurtosis among attributes of the numeric type.
474.81
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
0.24
Third quartile of kurtosis among attributes of the numeric type.
520.32
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.8
Percentage of numeric attributes.
493.94
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.
0.2
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.
0.09
Third quartile of skewness among attributes of the numeric type.
0.35
Maximum skewness among attributes of the numeric type.
0.6
Minimum standard deviation of attributes of the numeric type.
-0.05
First quartile of kurtosis among attributes of the numeric type.
35.55
Third quartile of standard deviation of attributes of the numeric type.
134
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.
479.96
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.
0.09
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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