Data
scpf

scpf

active ARFF Publicly available Visibility: public Uploaded 03-04-2019 by Quay Au
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  • 2019_multioutput_paper_benchmark_data
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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_views (target)numeric96 unique values
0 missing
num_votes (target)numeric11 unique values
0 missing
num_comments (target)numeric9 unique values
0 missing
daysUntilLastIssuenumeric268 unique values
0 missing
source_city_initiatednumeric2 unique values
263 missing
source_androidnumeric2 unique values
263 missing
source_remote_api_creatednumeric2 unique values
263 missing
source_new_map_widgetnumeric2 unique values
263 missing
source_iphonenumeric2 unique values
263 missing
tag_type_treenumeric2 unique values
794 missing
tag_type_street_lightnumeric2 unique values
794 missing
tag_type_graffitinumeric2 unique values
794 missing
tag_type_potholenumeric2 unique values
794 missing
tag_type_signsnumeric2 unique values
794 missing
tag_type_overgrowthnumeric2 unique values
794 missing
tag_type_trafficnumeric2 unique values
794 missing
tag_type_trashnumeric2 unique values
794 missing
tag_type_blighted_propertynumeric2 unique values
794 missing
tag_type_sidewalknumeric2 unique values
794 missing
latitudenumeric1127 unique values
0 missing
longitudenumeric1125 unique values
0 missing
city_Oaklandnumeric2 unique values
0 missing
city_Chicagonumeric2 unique values
0 missing
city_NHnumeric2 unique values
0 missing
city_Richmondnumeric2 unique values
0 missing
distanceFromCenternumeric1127 unique values
0 missing

62 properties

1137
Number of instances (rows) of the dataset.
26
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
9255
Number of missing values in the dataset.
994
Number of instances with at least one value missing.
26
Number of numeric attributes.
0
Number of nominal attributes.
1.02
First quartile of skewness among attributes of the numeric type.
12.3
Mean of means among attributes of the numeric type.
0.19
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
11.37
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.1
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4.19
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
5.71
Mean standard deviation of attributes of the numeric type.
3.61
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.3
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.81
Minimum kurtosis among attributes of the numeric type.
87.42
Percentage of instances having missing values.
Third quartile of entropy among attributes.
387.19
Maximum kurtosis among attributes of the numeric type.
-88.03
Minimum of means among attributes of the numeric type.
31.31
Percentage of missing values.
31.07
Third quartile of kurtosis among attributes of the numeric type.
349.97
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
100
Percentage of numeric attributes.
0.43
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-1.87
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
5.73
Third quartile of skewness among attributes of the numeric type.
17.35
Maximum skewness among attributes of the numeric type.
0.09
Minimum standard deviation of attributes of the numeric type.
0.51
First quartile of kurtosis among attributes of the numeric type.
0.56
Third quartile of standard deviation of attributes of the numeric type.
88.96
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0.04
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
45.55
Mean kurtosis among attributes of the numeric type.

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