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scpf

scpf

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Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : This is a pre-processed version of the dataset used in Kaggles See Click Predict Fix competition (Kaggle 2013). It concerns the prediction of three target variables that represent the number of views, clicks and comments that a specific 311 issue will receive. The issues have been collected from 4 cities (Oakland, Richmond, New Haven, Chicago) in the US and span a period of 12 months (01 2012-12 2012). The version of the dataset that we use here is a random 1 percent sample of the data. In terms of features we use the number of days that an issues stayed online, the source from where the issue was created (e.g. android, iphone, remote api, etc.), the type of the issue (e.g. graffiti, pothole, trash, etc.), the geographical co-ordinates of the issue, the city it was published from and the distance from the city center. All multi-valued nominal variables were first transformed to binary and then rare binary variables (being true for less than 1 percent of the cases) were removed.

26 features

num_comments (target)numeric9 unique values
0 missing
num_votes (target)numeric11 unique values
0 missing
num_views (target)numeric96 unique values
0 missing
tag_type_trashnumeric2 unique values
794 missing
distanceFromCenternumeric1127 unique values
0 missing
city_Richmondnumeric2 unique values
0 missing
city_NHnumeric2 unique values
0 missing
city_Chicagonumeric2 unique values
0 missing
city_Oaklandnumeric2 unique values
0 missing
longitudenumeric1125 unique values
0 missing
latitudenumeric1127 unique values
0 missing
tag_type_sidewalknumeric2 unique values
794 missing
tag_type_blighted_propertynumeric2 unique values
794 missing
daysUntilLastIssuenumeric268 unique values
0 missing
tag_type_trafficnumeric2 unique values
794 missing
tag_type_overgrowthnumeric2 unique values
794 missing
tag_type_signsnumeric2 unique values
794 missing
tag_type_potholenumeric2 unique values
794 missing
tag_type_graffitinumeric2 unique values
794 missing
tag_type_street_lightnumeric2 unique values
794 missing
tag_type_treenumeric2 unique values
794 missing
source_iphonenumeric2 unique values
263 missing
source_new_map_widgetnumeric2 unique values
263 missing
source_remote_api_creatednumeric2 unique values
263 missing
source_androidnumeric2 unique values
263 missing
source_city_initiatednumeric2 unique values
263 missing
Show all 26 features

62 properties

NumberOfInstances
1137
Number of instances (rows) of the dataset.
NumberOfFeatures
26
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
9255
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
994
Number of instances with at least one value missing.
NumberOfNumericFeatures
26
Number of numeric attributes.
NumberOfSymbolicFeatures
0
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
87.42
Percentage of instances having missing values.
PercentageOfMissingValues
31.31
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.51
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.04
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
1.02
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.19
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
11.37
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.1
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
3.61
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.3
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
31.07
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.43
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
5.73
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.56
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
12.3
Mean of means among attributes of the numeric type.
ClassEntropy
Entropy of the target attribute values.
Dimensionality
0.02
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
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
387.19
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
349.97
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
17.35
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
88.96
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
45.55
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
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
4.19
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
5.71
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.81
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-88.03
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.87
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.09
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
Show all 62 properties

9 tasks

Clustering on scpf
0 runs - estimation_procedure: 50 times Clustering
Clustering on scpf
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Clustering on scpf
0 runs - estimation_procedure: 50 times Clustering
Clustering on scpf
0 runs - estimation_procedure: 50 times Clustering
Clustering on scpf
0 runs - estimation_procedure: 50 times Clustering
Clustering on scpf
0 runs - estimation_procedure: 50 times Clustering
Clustering on scpf
0 runs - estimation_procedure: 50 times Clustering
Clustering on scpf
0 runs - estimation_procedure: 50 times Clustering
Clustering on scpf
0 runs - estimation_procedure: 50 times Clustering
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