Data
madeline

madeline

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

260 features

class (target)nominal2 unique values
0 missing
V256numeric181 unique values
0 missing
V1numeric214 unique values
0 missing
V257numeric220 unique values
0 missing
V2numeric132 unique values
0 missing
V258numeric45 unique values
0 missing
V3numeric145 unique values
0 missing
V259numeric64 unique values
0 missing
V4numeric127 unique values
0 missing
V5numeric144 unique values
0 missing
V6numeric31 unique values
0 missing
V7numeric167 unique values
0 missing
V8numeric80 unique values
0 missing
V9numeric116 unique values
0 missing
V10numeric12 unique values
0 missing
V11numeric189 unique values
0 missing
V12numeric114 unique values
0 missing
V13numeric93 unique values
0 missing
V14numeric170 unique values
0 missing
V15numeric125 unique values
0 missing
V16numeric155 unique values
0 missing
V17numeric46 unique values
0 missing
V18numeric139 unique values
0 missing
V19numeric195 unique values
0 missing
V20numeric141 unique values
0 missing
V21numeric173 unique values
0 missing
V22numeric8 unique values
0 missing
V23numeric106 unique values
0 missing
V24numeric67 unique values
0 missing
V25numeric40 unique values
0 missing
V26numeric169 unique values
0 missing
V27numeric242 unique values
0 missing
V28numeric144 unique values
0 missing
V29numeric241 unique values
0 missing
V30numeric95 unique values
0 missing
V31numeric228 unique values
0 missing
V32numeric174 unique values
0 missing
V33numeric220 unique values
0 missing
V34numeric125 unique values
0 missing
V35numeric207 unique values
0 missing
V36numeric208 unique values
0 missing
V37numeric75 unique values
0 missing
V38numeric54 unique values
0 missing
V39numeric77 unique values
0 missing
V40numeric109 unique values
0 missing
V41numeric210 unique values
0 missing
V42numeric236 unique values
0 missing
V43numeric78 unique values
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V44numeric223 unique values
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V45numeric207 unique values
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V46numeric131 unique values
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V47numeric105 unique values
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V48numeric88 unique values
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V49numeric191 unique values
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V50numeric50 unique values
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V51numeric242 unique values
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V52numeric60 unique values
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V53numeric271 unique values
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V54numeric33 unique values
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V55numeric214 unique values
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V56numeric214 unique values
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V57numeric92 unique values
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V61numeric146 unique values
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V62numeric168 unique values
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V65numeric240 unique values
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V76numeric45 unique values
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V79numeric22 unique values
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0 missing
V81numeric48 unique values
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V82numeric154 unique values
0 missing
V83numeric176 unique values
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V84numeric425 unique values
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V85numeric106 unique values
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V86numeric454 unique values
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V87numeric54 unique values
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V88numeric77 unique values
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V89numeric132 unique values
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V90numeric42 unique values
0 missing
V91numeric190 unique values
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V92numeric179 unique values
0 missing
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0 missing
V94numeric207 unique values
0 missing
V95numeric134 unique values
0 missing
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V100numeric232 unique values
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V101numeric51 unique values
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V104numeric38 unique values
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V105numeric236 unique values
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V106numeric217 unique values
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V110numeric20 unique values
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V113numeric50 unique values
0 missing
V114numeric235 unique values
0 missing
V115numeric133 unique values
0 missing
V116numeric81 unique values
0 missing
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V118numeric87 unique values
0 missing
V119numeric166 unique values
0 missing
V120numeric236 unique values
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0 missing
V122numeric134 unique values
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V123numeric116 unique values
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V124numeric73 unique values
0 missing
V125numeric232 unique values
0 missing
V126numeric25 unique values
0 missing
V127numeric145 unique values
0 missing
V128numeric233 unique values
0 missing
V129numeric69 unique values
0 missing
V130numeric223 unique values
0 missing
V131numeric10 unique values
0 missing
V132numeric33 unique values
0 missing
V133numeric175 unique values
0 missing
V134numeric170 unique values
0 missing
V135numeric31 unique values
0 missing
V136numeric207 unique values
0 missing
V137numeric204 unique values
0 missing
V138numeric125 unique values
0 missing
V139numeric12 unique values
0 missing
V140numeric198 unique values
0 missing
V141numeric125 unique values
0 missing
V142numeric45 unique values
0 missing
V143numeric16 unique values
0 missing
V144numeric200 unique values
0 missing
V145numeric84 unique values
0 missing
V146numeric220 unique values
0 missing
V147numeric115 unique values
0 missing
V148numeric48 unique values
0 missing
V149numeric229 unique values
0 missing
V150numeric75 unique values
0 missing
V151numeric116 unique values
0 missing
V152numeric30 unique values
0 missing
V153numeric69 unique values
0 missing
V154numeric99 unique values
0 missing
V155numeric57 unique values
0 missing
V156numeric9 unique values
0 missing
V157numeric60 unique values
0 missing
V158numeric50 unique values
0 missing
V159numeric232 unique values
0 missing
V160numeric93 unique values
0 missing
V161numeric122 unique values
0 missing
V162numeric45 unique values
0 missing
V163numeric462 unique values
0 missing
V164numeric352 unique values
0 missing
V165numeric102 unique values
0 missing
V166numeric77 unique values
0 missing
V167numeric202 unique values
0 missing
V168numeric50 unique values
0 missing
V169numeric43 unique values
0 missing
V170numeric61 unique values
0 missing
V171numeric192 unique values
0 missing
V172numeric64 unique values
0 missing
V173numeric134 unique values
0 missing
V174numeric225 unique values
0 missing
V175numeric74 unique values
0 missing
V176numeric143 unique values
0 missing
V177numeric69 unique values
0 missing
V178numeric159 unique values
0 missing
V179numeric496 unique values
0 missing
V180numeric193 unique values
0 missing
V181numeric87 unique values
0 missing
V182numeric11 unique values
0 missing
V183numeric143 unique values
0 missing
V184numeric610 unique values
0 missing
V185numeric73 unique values
0 missing
V186numeric231 unique values
0 missing
V187numeric99 unique values
0 missing
V188numeric112 unique values
0 missing
V189numeric104 unique values
0 missing
V190numeric200 unique values
0 missing
V191numeric178 unique values
0 missing
V192numeric343 unique values
0 missing
V193numeric36 unique values
0 missing
V194numeric133 unique values
0 missing
V195numeric40 unique values
0 missing
V196numeric244 unique values
0 missing
V197numeric125 unique values
0 missing
V198numeric525 unique values
0 missing
V199numeric62 unique values
0 missing
V200numeric115 unique values
0 missing
V201numeric202 unique values
0 missing
V202numeric73 unique values
0 missing
V203numeric198 unique values
0 missing
V204numeric154 unique values
0 missing
V205numeric38 unique values
0 missing
V206numeric30 unique values
0 missing
V207numeric169 unique values
0 missing
V208numeric72 unique values
0 missing
V209numeric82 unique values
0 missing
V210numeric127 unique values
0 missing
V211numeric217 unique values
0 missing
V212numeric128 unique values
0 missing
V213numeric244 unique values
0 missing
V214numeric92 unique values
0 missing
V215numeric143 unique values
0 missing
V216numeric153 unique values
0 missing
V217numeric243 unique values
0 missing
V218numeric229 unique values
0 missing
V219numeric203 unique values
0 missing
V220numeric181 unique values
0 missing
V221numeric9 unique values
0 missing
V222numeric224 unique values
0 missing
V223numeric119 unique values
0 missing
V224numeric208 unique values
0 missing
V225numeric116 unique values
0 missing
V226numeric134 unique values
0 missing
V227numeric233 unique values
0 missing
V228numeric65 unique values
0 missing
V229numeric251 unique values
0 missing
V230numeric240 unique values
0 missing
V231numeric120 unique values
0 missing
V232numeric219 unique values
0 missing
V233numeric309 unique values
0 missing
V234numeric182 unique values
0 missing
V235numeric71 unique values
0 missing
V236numeric99 unique values
0 missing
V237numeric95 unique values
0 missing
V238numeric255 unique values
0 missing
V239numeric203 unique values
0 missing
V240numeric78 unique values
0 missing
V241numeric83 unique values
0 missing
V242numeric85 unique values
0 missing
V243numeric208 unique values
0 missing
V244numeric100 unique values
0 missing
V245numeric223 unique values
0 missing
V246numeric72 unique values
0 missing
V247numeric166 unique values
0 missing
V248numeric134 unique values
0 missing
V249numeric89 unique values
0 missing
V250numeric120 unique values
0 missing
V251numeric203 unique values
0 missing
V252numeric235 unique values
0 missing
V253numeric438 unique values
0 missing
V254numeric230 unique values
0 missing
V255numeric236 unique values
0 missing

62 properties

3140
Number of instances (rows) of the dataset.
260
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.
259
Number of numeric attributes.
1
Number of nominal attributes.
0.08
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.09
Second quartile (Median) of kurtosis 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.
485.52
Second quartile (Median) of means among attributes of the numeric type.
50.29
Percentage of instances belonging to the most frequent class.
25.86
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
1579
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.04
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.96
Minimum kurtosis among attributes of the numeric type.
0.38
Percentage of binary attributes.
22.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.48
Maximum kurtosis among attributes of the numeric type.
475.9
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
517.31
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.18
Third quartile of kurtosis 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.
99.62
Percentage of numeric attributes.
495.78
Third quartile of means among attributes of the numeric type.
2
The maximum number of distinct values among attributes of the nominal type.
-0.12
Minimum skewness among attributes of the numeric type.
0.38
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.19
Maximum skewness among attributes of the numeric type.
0.94
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.07
Third quartile of skewness among attributes of the numeric type.
133.57
Maximum standard deviation of attributes of the numeric type.
49.71
Percentage of instances belonging to the least frequent class.
0.03
First quartile of kurtosis among attributes of the numeric type.
36.53
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
1561
Number of instances belonging to the least frequent class.
480.36
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.
0.06
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
488.48
Mean of means among attributes of the numeric type.
-0
First quartile of skewness among attributes of the numeric type.
0.5
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
11.69
First quartile of standard deviation of attributes of the numeric type.
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.
Second quartile (Median) of entropy among attributes.

6 tasks

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