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philippine

philippine

active ARFF Publicly available Visibility: public Uploaded 15-08-2018 by Janek Thomas
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The goal of this challenge is to expose the research community to real world datasets of interest to 4Paradigm. All datasets are formatted in a uniform way, though the type of data might differ. The data are provided as preprocessed matrices, so that participants can focus on classification, although participants are welcome to use additional feature extraction procedures (as long as they do not violate any rule of the challenge). All problems are binary classification problems and are assessed with the normalized Area Under the ROC Curve (AUC) metric (i.e. 2*AUC-1). The identity of the datasets and the type of data is concealed, though its structure is revealed. The final score in phase 2 will be the average of rankings on all testing datasets, a ranking will be generated from such results, and winners will be determined according to such ranking. The tasks are constrained by a time budget. The Codalab platform provides computational resources shared by all participants. Each code submission will be exceuted in a compute worker with the following characteristics: 2Cores / 8G Memory / 40G SSD with Ubuntu OS. To ensure the fairness of the evaluation, when a code submission is evaluated, its execution time is limited in time. http://automl.chalearn.org/data

309 features

class (target)nominal2 unique values
0 missing
V256numeric5740 unique values
0 missing
V1numeric5784 unique values
0 missing
V257numeric5776 unique values
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V2numeric718 unique values
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V259numeric5790 unique values
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V260numeric5808 unique values
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V265numeric5794 unique values
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V266numeric5780 unique values
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V267numeric5715 unique values
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V95numeric5462 unique values
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V148numeric5797 unique values
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V149numeric1558 unique values
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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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V163numeric5822 unique values
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0 missing
V165numeric5817 unique values
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V166numeric5730 unique values
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V167numeric5817 unique values
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V168numeric90 unique values
0 missing
V169numeric5780 unique values
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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
V185numeric4587 unique values
0 missing
V186numeric5767 unique values
0 missing
V187numeric5790 unique values
0 missing
V188numeric5815 unique values
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V189numeric5770 unique values
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V191numeric5472 unique values
0 missing
V192numeric5728 unique values
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V193numeric5798 unique values
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V195numeric5492 unique values
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V196numeric5363 unique values
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V198numeric5794 unique values
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0 missing
V202numeric179 unique values
0 missing
V203numeric5795 unique values
0 missing
V204numeric5788 unique values
0 missing
V205numeric5688 unique values
0 missing
V206numeric5816 unique values
0 missing
V207numeric5115 unique values
0 missing
V208numeric5727 unique values
0 missing
V209numeric5720 unique values
0 missing
V210numeric3629 unique values
0 missing
V211numeric5815 unique values
0 missing
V212numeric5501 unique values
0 missing
V213numeric758 unique values
0 missing
V214numeric5782 unique values
0 missing
V215numeric5781 unique values
0 missing
V216numeric5787 unique values
0 missing
V217numeric2741 unique values
0 missing
V218numeric736 unique values
0 missing
V219numeric5394 unique values
0 missing
V220numeric5774 unique values
0 missing
V221numeric5787 unique values
0 missing
V222numeric5754 unique values
0 missing
V223numeric5737 unique values
0 missing
V224numeric2454 unique values
0 missing
V225numeric3724 unique values
0 missing
V226numeric4618 unique values
0 missing
V227numeric5458 unique values
0 missing
V228numeric5816 unique values
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V229numeric5789 unique values
0 missing
V230numeric4046 unique values
0 missing
V231numeric31 unique values
0 missing
V232numeric2397 unique values
0 missing
V233numeric99 unique values
0 missing
V234numeric5725 unique values
0 missing
V235numeric5811 unique values
0 missing
V236numeric5792 unique values
0 missing
V237numeric5785 unique values
0 missing
V238numeric5419 unique values
0 missing
V239numeric5425 unique values
0 missing
V240numeric5747 unique values
0 missing
V241numeric5120 unique values
0 missing
V242numeric5774 unique values
0 missing
V243numeric5799 unique values
0 missing
V244numeric4051 unique values
0 missing
V245numeric5673 unique values
0 missing
V246numeric5708 unique values
0 missing
V247numeric5785 unique values
0 missing
V248numeric5751 unique values
0 missing
V249numeric5780 unique values
0 missing
V250numeric5109 unique values
0 missing
V251numeric4407 unique values
0 missing
V252numeric91 unique values
0 missing
V253numeric5787 unique values
0 missing
V254numeric5791 unique values
0 missing
V255numeric5736 unique values
0 missing

62 properties

5832
Number of instances (rows) of the dataset.
309
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.
308
Number of numeric attributes.
1
Number of nominal attributes.
0
First quartile of skewness among attributes of the numeric type.
6707.76
Mean of means among attributes of the numeric type.
0.11
First quartile of standard deviation of attributes of the numeric type.
0.5
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
1
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.
1.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.05
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
4.47
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.
2.72
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
50
Percentage of instances belonging to the most frequent class.
3750.13
Mean standard deviation of attributes of the numeric type.
0.58
Second quartile (Median) of skewness among attributes of the numeric type.
2916
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.32
Percentage of binary attributes.
2.73
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.26
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
4255.39
Maximum kurtosis among attributes of the numeric type.
-1.14
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
13.12
Third quartile of kurtosis among attributes of the numeric type.
202871.81
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.68
Percentage of numeric attributes.
120.51
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.
0.32
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.
-7.71
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.28
Third quartile of skewness among attributes of the numeric type.
62.13
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
-0.12
First quartile of kurtosis among attributes of the numeric type.
44.47
Third quartile of standard deviation of attributes of the numeric type.
95601.73
Maximum standard deviation of attributes of the numeric type.
50
Percentage of instances belonging to the least frequent class.
2916
Number of instances belonging to the least frequent class.
0.06
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.
102.36
Mean kurtosis among attributes of the numeric type.

6 tasks

0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - 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
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