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collins

collins

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Felicia West
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

23 features

binaryClass (target)nominal2 unique values
0 missing
LinearGuidancenumeric352 unique values
0 missing
Corpusnominal1 unique values
0 missing
Counternumeric500 unique values
0 missing
Genrenominal15 unique values
0 missing
Text_Coveragenumeric443 unique values
0 missing
ShiftingEventsnumeric132 unique values
0 missing
TimeIntervalnumeric173 unique values
0 missing
PastEventsnumeric283 unique values
0 missing
Motionnumeric123 unique values
0 missing
SpaceIntervalnumeric228 unique values
0 missing
WordPicturenumeric371 unique values
0 missing
Text (ignore)nominal500 unique values
0 missing
Notifyingnumeric218 unique values
0 missing
Interactingnumeric160 unique values
0 missing
Direct_Activitynumeric78 unique values
0 missing
Share_SocTiesnumeric260 unique values
0 missing
Reasoningnumeric262 unique values
0 missing
ThinkBacknumeric130 unique values
0 missing
ThinkAheadnumeric172 unique values
0 missing
ThinkNegativenumeric216 unique values
0 missing
ThinkPositivenumeric154 unique values
0 missing
InnerThinkingnumeric262 unique values
0 missing
FirstPersonnumeric139 unique values
0 missing
Show all 23 features

107 properties

NumberOfInstances
500
Number of instances (rows) of the dataset.
NumberOfFeatures
23
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
20
Number of numeric attributes.
NumberOfSymbolicFeatures
3
Number of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.99
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
0.01
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
0.98
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
0.99
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
0.01
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
0.98
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
0.99
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
0.01
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
0.98
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
0.63
Entropy of the target attribute values.
DecisionStumpAUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.05
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
2
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
84
Percentage of instances belonging to the most frequent class.
MajorityClassSize
420
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
3.65
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
29.62
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
250.5
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.63
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
15
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
4.22
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
144.48
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.82
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
3.77
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
15.67
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.32
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
4.75
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
6
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.16
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
8.22
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
-0
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.2
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.28
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
1
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.07
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.27
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
16
Percentage of instances belonging to the least frequent class.
MinorityClassSize
80
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
4.35
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
86.96
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
13.04
Percentage of nominal attributes.
Quartile1AttributeEntropy
-0
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.22
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.67
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.48
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.43
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.82
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.74
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
1.33
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.32
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.8
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.77
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
3.65
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
3.43
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
2.69
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.63
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.49
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1.05
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
7.81
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

Supervised Classification on collins
141 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Classification on collins
0 runs - estimation_procedure: 33% Holdout set - target_feature: binaryClass
Supervised Data Stream Classification on collins
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
Clustering on collins
0 runs
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
Clustering on collins
0 runs - estimation_procedure: 50 times Clustering
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