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ionosphere

ionosphere

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Author: Space Physics Group, Applied Physics Laboratory, Johns Hopkins University. Donated by Vince Sigillito. Source: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/ionosphere) Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Johns Hopkins University Ionosphere database This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased array of 16 high-frequency antennas with a total transmitted power on the order of 6.4 kilowatts. See the paper for more details. ### Attribute information Received signals were processed using an autocorrelation function whose arguments are the time of a pulse and the pulse number. There were 17 pulse numbers for the Goose Bay system. Instances in this database are described by 2 attributes per pulse number, corresponding to the complex values returned by the function resulting from the complex electromagnetic signal. The targets were free electrons in the ionosphere. "Good" (g) radar returns are those showing evidence of some type of structure in the ionosphere. "Bad" (b) returns are those that do not; their signals pass through the ionosphere. ### Relevant papers Sigillito, V. G., Wing, S. P., Hutton, L. V., & Baker, K. B. (1989). Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Technical Digest, 10, 262-266.

35 features

class (target)nominal2 unique values
0 missing
a19numeric254 unique values
0 missing
a18numeric280 unique values
0 missing
a20numeric266 unique values
0 missing
a21numeric248 unique values
0 missing
a22numeric265 unique values
0 missing
a23numeric248 unique values
0 missing
a24numeric264 unique values
0 missing
a25numeric256 unique values
0 missing
a26numeric273 unique values
0 missing
a27numeric256 unique values
0 missing
a28numeric281 unique values
0 missing
a29numeric244 unique values
0 missing
a30numeric266 unique values
0 missing
a31numeric243 unique values
0 missing
a32numeric263 unique values
0 missing
a33numeric245 unique values
0 missing
a34numeric263 unique values
0 missing
a10numeric267 unique values
0 missing
a02numeric1 unique values
0 missing
a03numeric219 unique values
0 missing
a04numeric269 unique values
0 missing
a05numeric204 unique values
0 missing
a06numeric259 unique values
0 missing
a07numeric231 unique values
0 missing
a08numeric260 unique values
0 missing
a09numeric244 unique values
0 missing
a01numeric2 unique values
0 missing
a11numeric246 unique values
0 missing
a12numeric269 unique values
0 missing
a13numeric238 unique values
0 missing
a14numeric266 unique values
0 missing
a15numeric234 unique values
0 missing
a16numeric270 unique values
0 missing
a17numeric254 unique values
0 missing
Show all 35 features

107 properties

NumberOfInstances
351
Number of instances (rows) of the dataset.
NumberOfFeatures
35
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
34
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.28
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.88
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
0.1
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
0.78
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
0.88
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
0.1
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
0.78
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
0.88
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
0.1
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
0.78
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
0.94
Entropy of the target attribute values.
DecisionStumpAUC
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.1
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
64.1
Percentage of instances belonging to the most frequent class.
MajorityClassSize
225
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
4.44
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.89
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
0.11
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.65
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.36
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0.25
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
-0.58
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.51
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.67
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.07
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
-2.53
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
35.9
Percentage of instances belonging to the least frequent class.
MinorityClassSize
126
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
2.86
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
97.14
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
2.86
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.21
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.01
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
-0.88
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
0.01
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.26
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
-0.61
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.52
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.62
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.4
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
0.02
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.57
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.75
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
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

28 tasks

Supervised Classification on ionosphere
1206 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on ionosphere
366 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on ionosphere
357 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on ionosphere
207 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on ionosphere
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on ionosphere
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on ionosphere
211 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on ionosphere
87 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on ionosphere
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on ionosphere
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on ionosphere
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on ionosphere
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on ionosphere
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on ionosphere
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on ionosphere
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on ionosphere
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on ionosphere
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on ionosphere
0 runs
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
Clustering on ionosphere
0 runs - estimation_procedure: 50 times Clustering
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