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Titanic

Titanic

active ARFF public Visibility: public Uploaded 06-04-2017 by Arnold
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PMLB version of the Titanic dataset, which only uses 3 features. See version 1 for the complete version: https://www.openml.org/d/40945

4 features

class (target)nominal2 unique values
0 missing
Classnumeric4 unique values
0 missing
Agenumeric2 unique values
0 missing
Sexnumeric2 unique values
0 missing
Show all 4 features

62 properties

NumberOfInstances
2201
Number of instances (rows) of the dataset.
NumberOfFeatures
4
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
3
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
25
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
75
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
25
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.76
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
-1.4
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.04
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-0
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
-0.7
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
15.28
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0
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
4.16
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.57
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
-0
Mean of means among attributes of the numeric type.
ClassEntropy
0.91
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
67.7
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1490
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
15.28
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0
Maximum of means among attributes of the numeric type.
MaxMutualInformation
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
4.16
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
4.83
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
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
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.68
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.76
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
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.4
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
32.3
Percentage of instances belonging to the least frequent class.
MinorityClassSize
711
Number of instances belonging to the least frequent class.
Show all 62 properties

23 tasks

Supervised Classification on Titanic
35 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on Titanic
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on Titanic
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on Titanic
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Titanic
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Titanic
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Titanic
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Titanic
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Titanic
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Titanic
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Titanic
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on Titanic
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
Clustering on Titanic
0 runs
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
Clustering on Titanic
0 runs - estimation_procedure: 50 times Clustering
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