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volkert

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SOURCE: [ChaLearn Automatic Machine Learning Challenge (AutoML)](https://competitions.codalab.org/competitions/2321), [ChaLearn](https://automl.chalearn.org/data) This is a "supervised learning" challenge in machine learning. We are making available 30 datasets, all pre-formatted in given feature representations (this means that each example consists of a fixed number of numerical coefficients). The challenge is to solve classification and regression problems, without any further human intervention. The difficulty is that there is a broad diversity of data types and distributions (including balanced or unbalanced classes, sparse or dense feature representations, with or without missing values or categorical variables, various metrics of evaluation, various proportions of number of features and number of examples). The problems are drawn from a wide variety of domains and include medical diagnosis from laboratory analyses, speech recognition, credit rating, prediction or drug toxicity or efficacy, classification of text, prediction of customer satisfaction, object recognition, protein structure prediction, action recognition in video data, etc. While there exist machine learning toolkits including methods that can solve all these problems, it is still considerable human effort to find, for a given combination of dataset, task, metric of evaluation, and available computational time, the combination of methods and hyper-parameter setting that is best suited. Your challenge is to create the "perfect black box" eliminating the human in the loop. This is a challenge with code submission: your code will be executed automatically on our servers to train and test your learning machines with unknown datasets. However, there is NO OBLIGATION TO SUBMIT CODE. Half of the prizes can be won by just submitting prediction results. There are six rounds (Prep, Novice, Intermediate, Advanced, Expert, and Master) in which datasets of progressive difficulty are introduced (5 per round). There is NO PREREQUISITE TO PARTICIPATE IN PREVIOUS ROUNDS to enter a new round. The rounds alternate AutoML phases in which submitted code is "blind tested" in limited time on our platform, using datasets you have never seen before, and Tweakathon phases giving you time to improve your methods by tweaking them on those datasets and running them on your own systems (without computational resource limitation). NOTE: This dataset corresponds to one of the datasets of the challenge.

181 features

class (target)nominal10 unique values
0 missing
V1numeric3441 unique values
0 missing
V2numeric1 unique values
0 missing
V3numeric1 unique values
0 missing
V4numeric1 unique values
0 missing
V5numeric1 unique values
0 missing
V6numeric1 unique values
0 missing
V7numeric1 unique values
0 missing
V8numeric1 unique values
0 missing
V9numeric1 unique values
0 missing
V10numeric1406 unique values
0 missing
V11numeric1 unique values
0 missing
V12numeric1 unique values
0 missing
V13numeric1 unique values
0 missing
V14numeric1 unique values
0 missing
V15numeric1 unique values
0 missing
V16numeric1 unique values
0 missing
V17numeric1 unique values
0 missing
V18numeric3445 unique values
0 missing
V19numeric1 unique values
0 missing
V20numeric1 unique values
0 missing
V21numeric1 unique values
0 missing
V22numeric1 unique values
0 missing
V23numeric1 unique values
0 missing
V24numeric1 unique values
0 missing
V25numeric1 unique values
0 missing
V26numeric1 unique values
0 missing
V27numeric1 unique values
0 missing
V28numeric1 unique values
0 missing
V29numeric1 unique values
0 missing
V30numeric1 unique values
0 missing
V31numeric1 unique values
0 missing
V32numeric1 unique values
0 missing
V33numeric1 unique values
0 missing
V34numeric1 unique values
0 missing
V35numeric1 unique values
0 missing
V36numeric1 unique values
0 missing
V37numeric3255 unique values
0 missing
V38numeric3322 unique values
0 missing
V39numeric3016 unique values
0 missing
V40numeric2692 unique values
0 missing
V41numeric2527 unique values
0 missing
V42numeric2308 unique values
0 missing
V43numeric2201 unique values
0 missing
V44numeric1873 unique values
0 missing
V45numeric1771 unique values
0 missing
V46numeric1621 unique values
0 missing
V47numeric1535 unique values
0 missing
V48numeric1440 unique values
0 missing
V49numeric1382 unique values
0 missing
V50numeric1261 unique values
0 missing
V51numeric1134 unique values
0 missing
V52numeric981 unique values
0 missing
V53numeric969 unique values
0 missing
V54numeric832 unique values
0 missing
V55numeric775 unique values
0 missing
V56numeric699 unique values
0 missing
V57numeric669 unique values
0 missing
V58numeric653 unique values
0 missing
V59numeric581 unique values
0 missing
V60numeric547 unique values
0 missing
V61numeric513 unique values
0 missing
V62numeric454 unique values
0 missing
V63numeric433 unique values
0 missing
V64numeric411 unique values
0 missing
V65numeric410 unique values
0 missing
V66numeric416 unique values
0 missing
V67numeric511 unique values
0 missing
V68numeric1067 unique values
0 missing
V69numeric1511 unique values
0 missing
V70numeric2443 unique values
0 missing
V71numeric2292 unique values
0 missing
V72numeric2499 unique values
0 missing
V73numeric2717 unique values
0 missing
V74numeric2880 unique values
0 missing
V75numeric2849 unique values
0 missing
V76numeric2764 unique values
0 missing
V77numeric2617 unique values
0 missing
V78numeric2434 unique values
0 missing
V79numeric2423 unique values
0 missing
V80numeric2224 unique values
0 missing
V81numeric2073 unique values
0 missing
V82numeric1866 unique values
0 missing
V83numeric1859 unique values
0 missing
V84numeric1493 unique values
0 missing
V85numeric29467 unique values
0 missing
V86numeric29768 unique values
0 missing
V87numeric29959 unique values
0 missing
V88numeric29768 unique values
0 missing
V89numeric38508 unique values
0 missing
V90numeric38849 unique values
0 missing
V91numeric38716 unique values
0 missing
V92numeric38849 unique values
0 missing
V93numeric8538 unique values
0 missing
V94numeric9769 unique values
0 missing
V95numeric8480 unique values
0 missing
V96numeric9769 unique values
0 missing
V97numeric8322 unique values
0 missing
V98numeric6497 unique values
0 missing
V99numeric8272 unique values
0 missing
V100numeric6497 unique values
0 missing
V101numeric18735 unique values
0 missing
V102numeric20006 unique values
0 missing
V103numeric18214 unique values
0 missing
V104numeric20006 unique values
0 missing
V105numeric36865 unique values
0 missing
V106numeric39120 unique values
0 missing
V107numeric35892 unique values
0 missing
V108numeric39120 unique values
0 missing
V109numeric3159 unique values
0 missing
V110numeric1295 unique values
0 missing
V111numeric1539 unique values
0 missing
V112numeric1696 unique values
0 missing
V113numeric1678 unique values
0 missing
V114numeric1873 unique values
0 missing
V115numeric1382 unique values
0 missing
V116numeric1975 unique values
0 missing
V117numeric1595 unique values
0 missing
V118numeric1354 unique values
0 missing
V119numeric2341 unique values
0 missing
V120numeric1273 unique values
0 missing
V121numeric1537 unique values
0 missing
V122numeric1783 unique values
0 missing
V123numeric1627 unique values
0 missing
V124numeric1532 unique values
0 missing
V125numeric1285 unique values
0 missing
V126numeric2630 unique values
0 missing
V127numeric2015 unique values
0 missing
V128numeric1365 unique values
0 missing
V129numeric1626 unique values
0 missing
V130numeric1685 unique values
0 missing
V131numeric1843 unique values
0 missing
V132numeric1668 unique values
0 missing
V133numeric1499 unique values
0 missing
V134numeric2394 unique values
0 missing
V135numeric1632 unique values
0 missing
V136numeric1791 unique values
0 missing
V137numeric2084 unique values
0 missing
V138numeric1813 unique values
0 missing
V139numeric2156 unique values
0 missing
V140numeric2091 unique values
0 missing
V141numeric2193 unique values
0 missing
V142numeric2247 unique values
0 missing
V143numeric2062 unique values
0 missing
V144numeric3427 unique values
0 missing
V145numeric2793 unique values
0 missing
V146numeric2029 unique values
0 missing
V147numeric2211 unique values
0 missing
V148numeric2194 unique values
0 missing
V149numeric2092 unique values
0 missing
V150numeric2193 unique values
0 missing
V151numeric1804 unique values
0 missing
V152numeric2078 unique values
0 missing
V153numeric1765 unique values
0 missing
V154numeric1607 unique values
0 missing
V155numeric2372 unique values
0 missing
V156numeric1493 unique values
0 missing
V157numeric1639 unique values
0 missing
V158numeric1812 unique values
0 missing
V159numeric1684 unique values
0 missing
V160numeric1589 unique values
0 missing
V161numeric1362 unique values
0 missing
V162numeric2576 unique values
0 missing
V163numeric2083 unique values
0 missing
V164numeric1306 unique values
0 missing
V165numeric1533 unique values
0 missing
V166numeric1627 unique values
0 missing
V167numeric1773 unique values
0 missing
V168numeric1482 unique values
0 missing
V169numeric1250 unique values
0 missing
V170numeric2355 unique values
0 missing
V171numeric1298 unique values
0 missing
V172numeric1554 unique values
0 missing
V173numeric1949 unique values
0 missing
V174numeric1366 unique values
0 missing
V175numeric1845 unique values
0 missing
V176numeric1655 unique values
0 missing
V177numeric1669 unique values
0 missing
V178numeric1508 unique values
0 missing
V179numeric1255 unique values
0 missing
V180numeric3819 unique values
0 missing
Show first 100 features

62 properties

NumberOfInstances
58310
Number of instances (rows) of the dataset.
NumberOfFeatures
181
Number of attributes (columns) of the dataset.
NumberOfClasses
10
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
180
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.45
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.55
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
1.28
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.37
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
3.59
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.01
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.15
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
19.12
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.04
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
3.91
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.08
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.15
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
0.13
Mean of means among attributes of the numeric type.
ClassEntropy
2.96
Entropy of the target attribute values.
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
21.96
Percentage of instances belonging to the most frequent class.
MajorityClassSize
12806
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1338.97
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1.6
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
10
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
28.83
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.85
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
47.91
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
10
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
3.17
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.07
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.23
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
10
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.39
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
2.33
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1361
Number of instances belonging to the least frequent class.
Show all 62 properties

20 tasks

Supervised Classification on volkert
13 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_class_complexity - target_feature: class
Supervised Classification on volkert
2 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on volkert
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on volkert
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
Supervised Classification on volkert
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on volkert
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on volkert
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on volkert
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on volkert
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on volkert
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on volkert
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on volkert
0 runs - estimation_procedure: 50 times Clustering
Clustering on volkert
0 runs - estimation_procedure: 50 times Clustering
Clustering on volkert
0 runs - estimation_procedure: 50 times Clustering
Clustering on volkert
0 runs - estimation_procedure: 50 times Clustering
Clustering on volkert
0 runs - estimation_procedure: 50 times Clustering
Clustering on volkert
0 runs - estimation_procedure: 50 times Clustering
Clustering on volkert
0 runs - estimation_procedure: 50 times Clustering
Clustering on volkert
0 runs - estimation_procedure: 50 times Clustering
Clustering on volkert
0 runs - estimation_procedure: 50 times Clustering
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