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braziltourism

braziltourism

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Author: Source: Unknown - Please cite: analcatdata A collection of data sets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission consists of a zip file containing two versions of each of 84 data sets, plus this README file. Each data set is given in comma-delimited ASCII (.csv) form, and Microsoft Excel (.xls) form. NOTICE: These data sets may be used freely for scientific, educational and/or noncommercial purposes, provided suitable acknowledgment is given (by citing the above-named reference). Further details concerning the book, including information on statistical software (including sample S-PLUS/R and SAS code), are available at the web site http://www.stern.nyu.edu/~jsimonof/AnalCatData Information about the dataset CLASSTYPE: nominal CLASSINDEX: last Note: Quotes, Single-Quotes and Backslashes were removed, Blanks replaced with Underscores

9 features

Trips (target)nominal7 unique values
0 missing
Agenumeric53 unique values
1 missing
Sexnominal3 unique values
1 missing
Incomenumeric101 unique values
44 missing
Travel_costnumeric45 unique values
3 missing
Access_roadnominal2 unique values
0 missing
Activenominal7 unique values
2 missing
Passivenominal5 unique values
1 missing
Logged_incomenumeric101 unique values
44 missing
Show all 9 features

107 properties

NumberOfInstances
412
Number of instances (rows) of the dataset.
NumberOfFeatures
9
Number of attributes (columns) of the dataset.
NumberOfClasses
7
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
96
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
49
Number of instances with at least one value missing.
NumberOfNumericFeatures
4
Number of numeric attributes.
NumberOfSymbolicFeatures
5
Number of nominal attributes.
AutoCorrelation
0.99
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.49
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
-0.01
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
CfsSubsetEval_NaiveBayesAUC
0.49
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
-0.01
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
CfsSubsetEval_kNN1NAUC
0.49
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
-0.01
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
ClassEntropy
1.11
Entropy of the target attribute values.
DecisionStumpAUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.02
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
13.9
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
77.18
Percentage of instances belonging to the most frequent class.
MajorityClassSize
318
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1.96
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
12.89
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1035.67
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.17
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
7
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
3.07
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
768.35
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.36
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
5.32
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
297.93
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.08
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
16.03
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
4.8
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.11
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
227.48
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.67
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.28
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
6.64
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.02
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.88
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.24
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
11.11
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
11.89
Percentage of instances having missing values.
PercentageOfMissingValues
2.59
Percentage of missing values.
PercentageOfNumericFeatures
44.44
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
55.56
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.76
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.3
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
13.55
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.6
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
3.69
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.41
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
4.34
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
74.7
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.07
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.2
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
70.34
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.92
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
11.32
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
805.54
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.15
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
2.75
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
608.4
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.23
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.23
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.23
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
2.28
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

16 tasks

Supervised Classification on braziltourism
521 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Trips
Supervised Classification on braziltourism
321 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Trips
Supervised Classification on braziltourism
176 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Trips
Learning Curve on braziltourism
145 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Trips
Supervised Data Stream Classification on braziltourism
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: Trips
Clustering on braziltourism
0 runs
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
Clustering on braziltourism
0 runs - estimation_procedure: 50 times Clustering
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