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jasmine

jasmine

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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.

145 features

class (target)nominal2 unique values
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Show first 100 features

62 properties

NumberOfInstances
2984
Number of instances (rows) of the dataset.
NumberOfFeatures
145
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
8
Number of numeric attributes.
NumberOfSymbolicFeatures
137
Number of nominal attributes.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
94.48
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
5.52
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
94.48
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.26
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
3.57
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-0.56
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
1.24
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.63
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0.45
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
76.81
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-0.55
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
8.66
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.85
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
0.86
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
98.26
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
1.15
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
9.68
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.99
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.51
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
0.2
Mean of means among attributes of the numeric type.
ClassEntropy
1
Entropy of the target attribute values.
Dimensionality
0.05
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
41.91
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
50
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1492
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
108.47
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
2.17
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.23
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
9.96
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
2.18
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
0.52
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
58.07
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
137
Number of binary attributes.
MeanMutualInformation
0.02
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
20.67
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
6.27
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.94
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
1.37
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.56
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.97
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.38
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
50
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1492
Number of instances belonging to the least frequent class.
Show all 62 properties

19 tasks

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