Data
USCrime

USCrime

active ARFF Publicly available Visibility: public Uploaded 07-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Datasets of Data And Story Library, project illustrating use of basic statistic methods, converted to arff format by Hakan Kjellerstrand. Source: TunedIT: http://tunedit.org/repo/DASL DASL file http://lib.stat.cmu.edu/DASL/Datafiles/USCrime.html US Crime Reference: Vandaele, W. (1978) Participation in illegitimate activities: Erlich revisited. In Deterrence and incapacitation, Blumstein, A., Cohen, J. and Nagin, D., eds., Washington, D.C.: National Academy of Sciences, 270-335. Methods: A Primer, New York: Chapman & Hall, 11. Also found in: Hand, D.J., et al. (1994) A Handbook of Small Data Sets, London: Chapman & Hall, 101-103. Authorization: Contact author Description: These data are crime-related and demographic statistics for 47 US states in 1960. The data were collected from the FBI's Uniform Crime Report and other government agencies to determine how the variable crime rate depends on the other variables measured in the study. Number of cases: 47 Variable Names: R: Crime rate: # of offenses reported to police per million population Age: The number of males of age 14-24 per 1000 population S: Indicator variable for Southern states (0 = No, 1 = Yes) Ed: Mean # of years of schooling x 10 for persons of age 25 or older Ex0: 1960 per capita expenditure on police by state and local government Ex1: 1959 per capita expenditure on police by state and local government LF: Labor force participation rate per 1000 civilian urban males age 14-24 M: The number of males per 1000 females N: State population size in hundred thousands NW: The number of non-whites per 1000 population U1: Unemployment rate of urban males per 1000 of age 14-24 U2: Unemployment rate of urban males per 1000 of age 35-39 W: Median value of transferable goods and assets or family income in tens of $ X: The number of families per 1000 earning below 1/2 the median income

14 features

X (target)numeric42 unique values
0 missing
Rnumeric45 unique values
0 missing
Agenumeric31 unique values
0 missing
Snumeric2 unique values
0 missing
Ednumeric24 unique values
0 missing
Ex0numeric38 unique values
0 missing
Ex1numeric39 unique values
0 missing
LFnumeric40 unique values
0 missing
Mnumeric36 unique values
0 missing
Nnumeric35 unique values
0 missing
NWnumeric44 unique values
0 missing
U1numeric35 unique values
0 missing
U2numeric26 unique values
0 missing
Wnumeric46 unique values
0 missing

19 properties

47
Number of instances (rows) of the dataset.
14
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
14
Number of numeric attributes.
0
Number of nominal attributes.
0.3
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
-43.37
Average class difference between consecutive instances.
0
Percentage of missing values.

13 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: X
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: X
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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