Data

places

active
ARFF
Publicly available Visibility: public Uploaded 29-09-2014 by Joaquin Vanschoren

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This dataset is taken from the Places Rated Almanac, by Richard
Boyer and David Savageau, copyrighted and published by Rand McNally.
This book order (SBN) number is 0-528-88008-X.
The nine rating criteria used by Places Rated Almanac are:
```
Climate & Terrain
Housing
Health Care & Environment
Crime
Transportation
Education
The Arts
Recreation
Economics
```
For all but two of the above criteria, the higher the score, the
better. For Housing and Crime, the lower the score the better.
The scores are computed using the following component statistics for
each criterion (see the Places Rated Almanac for details):
Climate & Terrain: very hot and very cold months, seasonal
temperature variation, heating- and cooling-degree days, freezing
days, zero-degree days, ninety-degree days.
Housing: utility bills, property taxes, mortgage payments.
Health Care & Environment: per capita physicians, teaching hospitals,
medical schools, cardiac rehabilitation centers, comprehensive cancer
treatment centers, hospices, insurance/hospitalization costs index,
flouridation of drinking water, air pollution.
Crime: violent crime rate, property crime rate.
Transportation: daily commute, public transportation, Interstate
highways, air service, passenger rail service.
Education: pupil/teacher ratio in the public K-12 system, effort
index in K-12, accademic options in higher education.
The Arts: museums, fine arts and public radio stations, public
television stations, universities offering a degree or degrees in the
arts, symphony orchestras, theatres, opera companies, dance
companies, public libraries.
Recreation: good restaurants, public golf courses, certified lanes
for tenpin bowling, movie theatres, zoos, aquariums, family theme
parks, sanctioned automobile race tracks, pari-mutuel betting
attractions, major- and minor- league professional sports teams, NCAA
Division I football and basketball teams, miles of ocean or Great
Lakes coastline, inland water, national forests, national parks, or
national wildlife refuges, Consolidated Metropolitan Statistical Area
access.
Economics: average household income adjusted for taxes and living
costs, income growth, job growth.
The data are recorded in two ASCII files, PLACES.DAT, and
PLACES.KEY . The first file contains 329 observations, 9 columns
plus an index column. The index stands for the location. PLACES.KEY
gives the index in the first column and the associated name of the
place in the second column. All data analysis can be done with
numeric variables and the index, as read in from PLACES.DAT .
Alternatively, the numerical key can be replaced by the alphabetic
name, as given by PLACES.KEY .
Information about the dataset
CLASSTYPE: numeric
CLASSINDEX: 9

Economics (target) | numeric | 312 unique values 0 missing | |

Climate_and_Terrain | numeric | 199 unique values 0 missing | |

Housing | numeric | 319 unique values 0 missing | |

Health_Care_and_Environment | numeric | 302 unique values 0 missing | |

Crime | numeric | 291 unique values 0 missing | |

Transportation | numeric | 323 unique values 0 missing | |

Education | numeric | 276 unique values 0 missing | |

The_Arts | numeric | 318 unique values 0 missing | |

Recreation | numeric | 312 unique values 0 missing | |

Place (ignore) | nominal | 329 unique values 0 missing |

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Standard deviation of the number of distinct values among attributes of the nominal type.

Average number of distinct values among the attributes of the nominal type.

338.97

First quartile of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

1.2

Second quartile (Median) of kurtosis among attributes of the numeric type.

2814.89

Second quartile (Median) of means among attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

Minimal mutual information between the nominal attributes and the target attribute.

0.71

Second quartile (Median) of skewness among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

The minimal number of distinct values among attributes of the nominal type.

1003

Second quartile (Median) of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Maximum mutual information between the nominal attributes and the target attribute.

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

The maximum number of distinct values among attributes of the nominal type.

8.34

Third quartile of kurtosis among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Third quartile of mutual information between the nominal attributes and the target attribute.

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

2.36

Third quartile of skewness among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

0.84

First quartile of kurtosis among attributes of the numeric type.

1918.22

Third quartile of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Average mutual information between the nominal attributes and the target attribute.

First quartile of mutual information between the nominal attributes and the target attribute.

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.