Data
detroit

detroit

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Please cite: Data from StatLib (ftp stat.cmu.edu/datasets) This is the data set called `DETROIT' in the book `Subset selection in regression' by Alan J. Miller published in the Chapman & Hall series of monographs on Statistics & Applied Probability, no. 40. The data are unusual in that a subset of three predictors can be found which gives a very much better fit to the data than the subsets found from the Efroymson stepwise algorithm, or from forward selection or backward elimination. The original data were given in appendix A of `Regression analysis and its application: A data-oriented approach' by Gunst & Mason, Statistics textbooks and monographs no. 24, Marcel Dekker. It has caused problems because some copies of the Gunst & Mason book do not contain all of the data, and because Miller does not say which variables he used as predictors and which is the dependent variable. (HOM was the dependent variable, and the predictors were FTP ... WE) The data were collected by J.C. Fisher and used in his paper: "Homicide in Detroit: The Role of Firearms", Criminology, vol.14, 387-400 (1976) The data are on the homicide rate in Detroit for the years 1961-1973. FTP - Full-time police per 100,000 population UEMP - % unemployed in the population MAN - number of manufacturing workers in thousands LIC - Number of handgun licences per 100,000 population GR - Number of handgun registrations per 100,000 population CLEAR - % homicides cleared by arrests WM - Number of white males in the population NMAN - Number of non-manufacturing workers in thousands GOV - Number of government workers in thousands HE - Average hourly earnings WE - Average weekly earnings HOM - Number of homicides per 100,000 of population ACC - Death rate in accidents per 100,000 population ASR - Number of assaults per 100,000 population N.B. Each case takes two lines.

14 features

ASR (target)numeric13 unique values
0 missing
FTPnumeric13 unique values
0 missing
UEMPnumeric13 unique values
0 missing
MANnumeric13 unique values
0 missing
LICnumeric13 unique values
0 missing
GRnumeric13 unique values
0 missing
CLEARnumeric13 unique values
0 missing
WMnumeric13 unique values
0 missing
NMANnumeric13 unique values
0 missing
GOVnumeric13 unique values
0 missing
HEnumeric13 unique values
0 missing
WEnumeric13 unique values
0 missing
HOMnumeric13 unique values
0 missing
ACCnumeric13 unique values
0 missing

19 properties

13
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
Percentage of binary attributes.
0
Percentage of instances having missing values.
-29.1
Average class difference between consecutive instances.
0
Percentage of missing values.
1.08
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.

14 tasks

2 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: ASR
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: ASR
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: root_mean_squared_error - target_feature: HOM
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
Define a new task