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dionis

dionis

active ARFF Publicly available Visibility: public Uploaded 16-08-2018 by Heidi Smith
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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.

61 features

class (target)nominal355 unique values
0 missing
V46numeric58989 unique values
0 missing
V32numeric7478 unique values
0 missing
V33numeric1 unique values
0 missing
V34numeric8279 unique values
0 missing
V35numeric1 unique values
0 missing
V36numeric4925 unique values
0 missing
V37numeric1 unique values
0 missing
V38numeric16869 unique values
0 missing
V39numeric6229 unique values
0 missing
V40numeric30659 unique values
0 missing
V41numeric66076 unique values
0 missing
V42numeric52856 unique values
0 missing
V43numeric34775 unique values
0 missing
V44numeric4541 unique values
0 missing
V45numeric2254 unique values
0 missing
V30numeric49007 unique values
0 missing
V47numeric6838 unique values
0 missing
V48numeric59559 unique values
0 missing
V49numeric1694 unique values
0 missing
V50numeric62447 unique values
0 missing
V51numeric8165 unique values
0 missing
V52numeric4471 unique values
0 missing
V53numeric38 unique values
0 missing
V54numeric1 unique values
0 missing
V55numeric8499 unique values
0 missing
V56numeric63618 unique values
0 missing
V57numeric55797 unique values
0 missing
V58numeric5006 unique values
0 missing
V59numeric22748 unique values
0 missing
V60numeric1565 unique values
0 missing
V15numeric58575 unique values
0 missing
V1numeric4925 unique values
0 missing
V2numeric8044 unique values
0 missing
V3numeric1309 unique values
0 missing
V4numeric58054 unique values
0 missing
V5numeric4087 unique values
0 missing
V6numeric1182 unique values
0 missing
V7numeric20836 unique values
0 missing
V8numeric61135 unique values
0 missing
V9numeric4979 unique values
0 missing
V10numeric1886 unique values
0 missing
V11numeric9314 unique values
0 missing
V12numeric34664 unique values
0 missing
V13numeric16974 unique values
0 missing
V14numeric1 unique values
0 missing
V31numeric6888 unique values
0 missing
V16numeric8624 unique values
0 missing
V17numeric9123 unique values
0 missing
V18numeric41067 unique values
0 missing
V19numeric7747 unique values
0 missing
V20numeric3815 unique values
0 missing
V21numeric2967 unique values
0 missing
V22numeric7276 unique values
0 missing
V23numeric82253 unique values
0 missing
V24numeric40314 unique values
0 missing
V25numeric1459 unique values
0 missing
V26numeric2620 unique values
0 missing
V27numeric1 unique values
0 missing
V28numeric11383 unique values
0 missing
V29numeric9674 unique values
0 missing
Show all 61 features

62 properties

NumberOfInstances
416188
Number of instances (rows) of the dataset.
NumberOfFeatures
61
Number of attributes (columns) of the dataset.
NumberOfClasses
355
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
60
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
98.36
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
1.64
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.82
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-88.18
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
-1.7
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
412.69
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
1.8
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
82.27
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
-0.14
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1451.96
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
199587.81
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
3590.37
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
0.47
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
798265.78
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
1199.26
Mean of means among attributes of the numeric type.
ClassEntropy
8.45
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
0.59
Percentage of instances belonging to the most frequent class.
MajorityClassSize
2469
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
369550.74
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
10070.77
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
355
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
565.38
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
4007456.75
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
77960.01
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
355
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-40
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
730917.26
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.57
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-8372.24
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
355
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-586
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.21
Percentage of instances belonging to the least frequent class.
MinorityClassSize
878
Number of instances belonging to the least frequent class.
Show all 62 properties

18 tasks

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