Data
fl2000

fl2000

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Author: Source: Unknown - Date unknown Please cite: 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

17 features

technology (target)nominal5 unique values
0 missing
county (ignore)nominal67 unique values
0 missing
columnsnominal2 unique values
0 missing
undernumeric60 unique values
0 missing
overnumeric65 unique values
0 missing
Bushnumeric67 unique values
0 missing
Gorenumeric67 unique values
0 missing
Brownenumeric57 unique values
0 missing
Nadernumeric66 unique values
0 missing
Harrisnumeric22 unique values
0 missing
Hagelinnumeric36 unique values
0 missing
Buchanannumeric61 unique values
0 missing
McReynoldsnumeric18 unique values
0 missing
Phillipsnumeric31 unique values
0 missing
Mooreheadnumeric41 unique values
0 missing
Chotenumeric6 unique values
0 missing
McCarthynumeric3 unique values
0 missing

107 properties

67
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
5
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.
3
Number of nominal attributes.
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
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.85
Mean skewness among attributes of the numeric type.
0.86
Second quartile (Median) of entropy among attributes.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.22
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
61.19
Percentage of instances belonging to the most frequent class.
10102.95
Mean standard deviation of attributes of the numeric type.
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
1.49
Entropy of the target attribute values.
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
41
Number of instances belonging to the most frequent class.
0.86
Minimal entropy among attributes.
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
0.86
Maximum entropy among attributes.
2.27
Minimum kurtosis among attributes of the numeric type.
0.06
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.24
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
61.16
Maximum kurtosis among attributes of the numeric type.
0.09
Minimum of means among attributes of the numeric type.
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
43453.99
Maximum of means among attributes of the numeric type.
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
0.25
Number of attributes divided by the number of instances.
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.
5.88
Percentage of binary attributes.
0.86
Third quartile of entropy among attributes.
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.
1.71
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
35.87
Third quartile of kurtosis among attributes of the numeric type.
0.44
Average class difference between consecutive instances.
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
7.68
Maximum skewness among attributes of the numeric type.
0.42
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
1515.29
Third quartile of means among attributes of the numeric type.
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
0.27
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
75070.44
Maximum standard deviation of attributes of the numeric type.
1.49
Percentage of instances belonging to the least frequent class.
82.35
Percentage of numeric attributes.
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
0.86
Average entropy of the attributes.
1
Number of instances belonging to the least frequent class.
17.65
Percentage of nominal attributes.
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
19.53
Mean kurtosis among attributes of the numeric type.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.86
First quartile of entropy among attributes.
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
0.27
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
6541.18
Mean of means among attributes of the numeric type.
0.36
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
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.
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
9.05
First quartile of means among attributes of the numeric type.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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.
1
Number of binary attributes.
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.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.5
Average number of distinct values among the attributes of the nominal type.
2.2
First quartile of skewness among attributes of the numeric type.

6 tasks

32 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: technology
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: technology
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: technology
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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