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autoUniv-au1-1000

autoUniv-au1-1000

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Author: Ray. J. Hickey Source: UCI Please cite: * Dataset Title: AutoUniv Dataset data problem: autoUniv-au1-1000 * Abstract: AutoUniv is an advanced data generator for classifications tasks. The aim is to reflect the nuances and heterogeneity of real data. Data can be generated in .csv, ARFF or C4.5 formats. * Source: AutoUniv was developed by Ray. J. Hickey. Email: ray.j.hickey '@' gmail.com AutoUniv web-site: http://sites.google.com/site/autouniv/. * Data Set Information: The user first creates a classification model and then generates classified examples from it. To create a model, the following are specified: the number of attributes (up to 1000) and their type (discrete or continuous), the number of classes (up to 10), the complexity of the underlying rules and the noise level. AutoUniv then produces a model through a process of constrained randomised search to satisfy the user's requirements. A model can have up to 3000 rules. Rare class models can be designed. A sequence of models can be designed to reflect concept and/or population drift. AutoUniv creates three text files for a model: a Prolog specification of the model used to generate examples (.aupl); a user-friendly statement of the classification rules in an 'if ... then' format (.aurules); a statistical summary of the main properties of the model, including its Bayes rate (.auprops). * Attribute Information: Attributes may be discrete with up to 10 values or continuous. A discrete attribute can be nominal with values v1, v2, v3 ... or integer with values 0, 1, 2 , ... . * Relevant Papers: Marrs, G, Hickey, RJ and Black, MM (2010) Modeling the example life-cycle in an online classification learner. In Proceedings of HaCDAIS 2010: International Workshop on Handling Concept Drift in Adaptive Information Systems. [Web Link]#proc . Marrs, G, Hickey, RJ and Black, MM (2010) The Impact of Latency on Online Classification Learning with Concept Drift. In Y. Bi and M.A. Williams (Eds.): KSEM 2010, LNAI 6291, Springer-Verlag, Berlin, pp. 459–469. Hickey, RJ (2007) Structure and Majority Classes in Decision Tree Learning. Journal of Machine Learning Research, 8, pp. 1747-1768.

21 features

Class (target)nominal2 unique values
0 missing
V11numeric2 unique values
0 missing
V20numeric2 unique values
0 missing
V19numeric2 unique values
0 missing
V18numeric2 unique values
0 missing
V17numeric2 unique values
0 missing
V16numeric2 unique values
0 missing
V15numeric2 unique values
0 missing
V14numeric2 unique values
0 missing
V13numeric2 unique values
0 missing
V12numeric2 unique values
0 missing
V1numeric2 unique values
0 missing
V10numeric2 unique values
0 missing
V9numeric2 unique values
0 missing
V8numeric2 unique values
0 missing
V7numeric2 unique values
0 missing
V6numeric2 unique values
0 missing
V5numeric2 unique values
0 missing
V4numeric2 unique values
0 missing
V3numeric2 unique values
0 missing
V2numeric2 unique values
0 missing
Show all 21 features

107 properties

NumberOfInstances
1000
Number of instances (rows) of the dataset.
NumberOfFeatures
21
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
1
Number of nominal attributes.
AutoCorrelation
0.62
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.55
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
0.26
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
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
0.62
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
0.26
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
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
0.62
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
0.27
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
0.14
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
0.83
Entropy of the target attribute values.
DecisionStumpAUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.02
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
74.1
Percentage of instances belonging to the most frequent class.
MajorityClassSize
741
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
-0.15
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.7
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.36
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.5
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-1.72
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0.47
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.15
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.49
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-2
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.22
Minimum of means among attributes of the numeric type.
MinMutualInformation
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
-0.9
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.41
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
25.9
Percentage of instances belonging to the least frequent class.
MinorityClassSize
259
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.06
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
4.76
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
95.24
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
4.76
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-2
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.42
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.09
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.49
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-1.98
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.51
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-0.02
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.5
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-1.85
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.52
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.34
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.5
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
0.26
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
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
0.34
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

23 tasks

Supervised Classification on autoUniv-au1-1000
581 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on autoUniv-au1-1000
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on autoUniv-au1-1000
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
Learning Curve on autoUniv-au1-1000
45 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
Clustering on autoUniv-au1-1000
0 runs
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au1-1000
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on autoUniv-au1-1000
1299 runs - target_feature: Class
Subgroup Discovery on autoUniv-au1-1000
1299 runs - target_feature: Class
Subgroup Discovery on autoUniv-au1-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au1-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au1-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au1-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au1-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au1-1000
0 runs - target_feature: Class
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