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Publicly available Visibility: public Uploaded 28-09-2014 by Joaquin Vanschoren

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County data from the 2000 Presidential Election in Florida.
Compiled by Brett Presnell
Department of Statistics, University of Florida
These data are derived from three sources, described below. As far
as I am aware, you are free to use these data in any way that you
see fit, though some acknowledgement is always nice.
The candidate vote counts are the final certified counts reported
by the Florida Division of Elections. These were obtained from
the NORC web site in the file Cert_results.csv. Note that these
do NOT inculde the federal absentee votes (so that Gore's total
vote is actually higher here than Bush's).
The undervote and overvote counts were extracted from the NORC
ballot level data in the file aligned.txt. Since aligned.txt is
too large to work with in R (or almost any other program) I used
cut (a standard UNIX program) to extract just the columns I needed:
cut -f 2,9,10 -d"|" aligned.txt > tmp
Then I read the results into R and processed them there.
The technology and columns data were extracted from the Media
Group data from the NORC web site. "Technology" is simply the
type of voting machine used, and "columns" is 1 if the ballot
listed the presidential candidates in a single column on a single
page, and 2 if the presidential candidates were spread over two
columns or two pages of the ballot.
These agree with some earlier data that I had obtained from the NY
Times web site, except that in the media group data the PalmBeach
county ballot (the famous butterfly ballot) was listed as having
one column. I would definitely call this a two-column ballot, so
that is the designation recorded here. At one time I thought that
MiamiDade County also used a two-column ballot, but I was wrong
(the ballot listed the candidates and parties in English and
Spanish in opposing columns). Images of most of the ballots can
be found on the New York Times web site:
www.nytimes.com/images/2001/11/12/politics/recount/index_BALLOT.html
Information about the dataset
CLASSTYPE: nominal
CLASSINDEX: 2

technology (target) | nominal | 5 unique values 0 missing | |

county (ignore) | nominal | 67 unique values 0 missing | |

columns | nominal | 2 unique values 0 missing | |

under | numeric | 60 unique values 0 missing | |

over | numeric | 65 unique values 0 missing | |

Bush | numeric | 67 unique values 0 missing | |

Gore | numeric | 67 unique values 0 missing | |

Browne | numeric | 57 unique values 0 missing | |

Nader | numeric | 66 unique values 0 missing | |

Harris | numeric | 22 unique values 0 missing | |

Hagelin | numeric | 36 unique values 0 missing | |

Buchanan | numeric | 61 unique values 0 missing | |

McReynolds | numeric | 18 unique values 0 missing | |

Phillips | numeric | 31 unique values 0 missing | |

Moorehead | numeric | 41 unique values 0 missing | |

Chote | numeric | 6 unique values 0 missing | |

McCarthy | numeric | 3 unique values 0 missing |

26.93

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

0.79

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

0.24

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

0.55

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

13.12

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

0.52

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

139.46

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

0.79

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

0.74

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

0.06

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

0.24

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

3.35

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

0.52

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.51

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

0.06

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

190.64

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

0.68

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

0.06

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

2

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

0.34

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

23.23

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

5

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

35.87

Third quartile of kurtosis among attributes of the numeric type.

0.38

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

0.65

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

0.76

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

0.68

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

0.06

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

0.24

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

0.34

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

0.45

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

5.53

Third quartile of skewness among attributes of the numeric type.

0.55

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

0.38

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

0.65

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

0.81

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

2582.39

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

0.76

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

0.68

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

5.12

First quartile of kurtosis among attributes of the numeric type.

0.79

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

0.24

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

0.34

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

0.45

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

0.06

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

0.55

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

0.38

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

0.65

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

12.44

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

0.06

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

0.52

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.76

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

2.12

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

3.5

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