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squash-unstored

squash-unstored

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.

24 features

binaryClass (target)nominal2 unique values
0 missing
fructosenumeric45 unique values
6 missing
heat_input_flowernumeric13 unique values
0 missing
heat_input_emergnumeric13 unique values
0 missing
fibrenumeric50 unique values
1 missing
dry/moistnumeric52 unique values
0 missing
flavournumeric51 unique values
0 missing
sweetnessnumeric52 unique values
0 missing
starchnumeric46 unique values
6 missing
glucose+fructosenumeric46 unique values
6 missing
totalnumeric46 unique values
6 missing
sucrosenumeric46 unique values
6 missing
sitenominal3 unique values
0 missing
glucosenumeric46 unique values
6 missing
groundspot_a*numeric48 unique values
2 missing
fgddnumeric13 unique values
0 missing
egddnumeric13 unique values
0 missing
a*numeric43 unique values
0 missing
brixnumeric36 unique values
0 missing
solidsnumeric46 unique values
0 missing
penenumeric36 unique values
0 missing
weightnumeric49 unique values
0 missing
fruitnominal22 unique values
0 missing
dafnominal5 unique values
0 missing
Show all 24 features

107 properties

NumberOfInstances
52
Number of instances (rows) of the dataset.
NumberOfFeatures
24
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
39
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
9
Number of instances with at least one value missing.
NumberOfNumericFeatures
20
Number of numeric attributes.
NumberOfSymbolicFeatures
4
Number of nominal attributes.
AutoCorrelation
0.49
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.65
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
0.37
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
0.27
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
0.65
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
0.37
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
0.27
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
0.65
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
0.37
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
0.27
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
Entropy of the target attribute values.
DecisionStumpAUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.46
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
5.57
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
53.85
Percentage of instances belonging to the most frequent class.
MajorityClassSize
28
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
4.26
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1.07
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1735.98
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.43
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
22
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.92
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
325.27
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
2.69
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.22
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
303.24
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.18
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
14.07
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
8
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.26
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
68.58
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
1.58
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.83
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
4.79
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.04
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
-0.79
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
2.06
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
46.15
Percentage of instances belonging to the least frequent class.
MinorityClassSize
24
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
4.17
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
17.31
Percentage of instances having missing values.
PercentageOfMissingValues
3.13
Percentage of missing values.
PercentageOfNumericFeatures
83.33
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
16.67
Percentage of nominal attributes.
Quartile1AttributeEntropy
1.58
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.5
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
9.99
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.04
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.11
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
4.87
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
2.24
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
85.14
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.06
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.33
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
28.46
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
4.26
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.08
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
467.2
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.43
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.51
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
105.29
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.46
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.46
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.46
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
9.42
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.42
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

Supervised Classification on squash-unstored
496 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Classification on squash-unstored
191 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Data Stream Classification on squash-unstored
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
Clustering on squash-unstored
0 runs
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-unstored
0 runs - estimation_procedure: 50 times Clustering
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