Data
solar-flare

solar-flare

deactivated ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • study_1 study_37 study_41 study_70
Issue #Downvotes for this reason By
test1User 2


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Please cite: 1. TItle: Solar Flare database This is the second version of the dataset. It has more instances and has had much more error correction applied to it, and has consequently been treated as more reliable. 2. Source Information -- Donor: Gary Bradshaw -- Date: 3/89 3. Past Usage: -- Gary Bradshaw: (Class Attributes were collapsed to 0 and >0) -- See the past-usage file for a note written by Gary Bradshaw 4. Relevant Information: -- The database contains 3 potential classes, one for the number of times a certain type of solar flare occured in a 24 hour period. -- Each instance represents captured features for 1 active region on the sun. -- The data are divided into two sections. The second section (flare.data2) has had much more error correction applied to the it, and has consequently been treated as more reliable. 5. Number of Instances: flare.data1: 323, flare.data2: 1066 6. Number of attributes: 13 (includes 3 class attributes) 7. Attribute Information: 1. Code for class (modified Zurich class) (A,B,C,D,E,F,H) 2. Code for largest spot size (X,R,S,A,H,K) 3. Code for spot distribution (X,O,I,C) 4. Activity (1 = reduced, 2 = unchanged) 5. Evolution (1 = decay, 2 = no growth, 3 = growth) 6. Previous 24 hour flare activity code (1 = nothing as big as an M1, 2 = one M1, 3 = more activity than one M1) 7. Historically-complex (1 = Yes, 2 = No) 8. Did region become historically complex (1 = yes, 2 = no) on this pass across the sun's disk 9. Area (1 = small, 2 = large) 10. Area of the largest spot (1 = <=5, 2 = >5) From all these predictors three classes of flares are predicted, which are represented in the last three columns. 11. C-class flares production by this region Number in the following 24 hours (common flares) 12. M-class flares production by this region Number in the following 24 hours (moderate flares) 13. X-class flares production by this region Number in the following 24 hours (severe flares) 8. Missing values: None 9. Class Distribution: flare.data1: 0 1 2 4 Total C-class flares 287 29 7 0 323 M-class flares 291 24 6 2 323 X-class flares 316 7 0 0 323 flare.data2: 0 1 2 3 4 5 6 7 8 Total C-class flares 884 112 33 20 9 4 3 0 1 1066 M-class flares 1030 29 3 2 1 0 1 0 0 1066 X-class flares 1061 4 1 0 0 0 0 0 0 1066 Information about the dataset CLASSTYPE: nominal CLASSINDEX: first

13 features

X-class_flares_production_by_this_region (target)nominal3 unique values
0 missing
classnominal6 unique values
0 missing
largest_spot_sizenominal6 unique values
0 missing
spot_distributionnominal4 unique values
0 missing
Activitynominal2 unique values
0 missing
Evolutionnominal3 unique values
0 missing
Previous_24_hour_flare_activity_codenominal3 unique values
0 missing
Historically-complexnominal2 unique values
0 missing
Did_region_become_historically_complexnominal2 unique values
0 missing
Areanominal2 unique values
0 missing
Area_of_the_largest_spotnominal1 unique values
0 missing
C-class_flares_production_by_this_regionnominal8 unique values
0 missing
M-class_flares_production_by_this_regionnominal6 unique values
0 missing
Show all 13 features

107 properties

NumberOfInstances
1066
Number of instances (rows) of the dataset.
NumberOfFeatures
13
Number of attributes (columns) of the dataset.
NumberOfClasses
3
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
0
Number of numeric attributes.
NumberOfSymbolicFeatures
13
Number of nominal attributes.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
REPTreeDepth3Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
30.77
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
0.01
Number of attributes divided by the number of instances.
MaxMutualInformation
0.02
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
1
The minimal number of distinct values among attributes of the nominal type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
1.58
Third quartile of entropy among attributes.
RandomTreeDepth1ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
4.95
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
8
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.99
Average class difference between consecutive instances.
RandomTreeDepth1Kappa
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
0.45
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
RandomTreeDepth2AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.09
Percentage of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile3MutualInformation
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
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
RandomTreeDepth2ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
0.94
Average entropy of the attributes.
MinorityClassSize
1
Number of instances belonging to the least frequent class.
Quartile1AttributeEntropy
0.25
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
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
RandomTreeDepth2Kappa
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
0.45
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
RandomTreeDepth3AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
RandomTreeDepth3ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
0.01
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
RandomTreeDepth3Kappa
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNoiseToSignalRatio
99.23
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
4
Number of binary attributes.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NAUC
0.45
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
StdvNominalAttDistinctValues
2.14
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
3.69
Average number of distinct values among the attributes of the nominal type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
kNN1NAUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
Quartile2AttributeEntropy
0.78
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
-0
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
kNN1NErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
99.53
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
0.05
Entropy of the target attribute values.
kNN1NKappa
-0
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
1061
Number of instances belonging to the most frequent class.
MinAttributeEntropy
0
Minimal entropy among attributes.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3AUC
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
2.36
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
Quartile2MutualInformation
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
REPTreeDepth3ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
Show all 107 properties

29 tasks

Supervised Classification on solar-flare
1232 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: X-class_flares_production_by_this_region
Supervised Classification on solar-flare
311 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: X-class_flares_production_by_this_region
Supervised Classification on solar-flare
191 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: X-class_flares_production_by_this_region
Supervised Classification on solar-flare
176 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on solar-flare
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Learning Curve on solar-flare
300 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: X-class_flares_production_by_this_region
Learning Curve on solar-flare
99 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on solar-flare
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on solar-flare
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on solar-flare
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on solar-flare
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on solar-flare
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on solar-flare
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on solar-flare
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on solar-flare
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on solar-flare
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: X-class_flares_production_by_this_region
Supervised Data Stream Classification on solar-flare
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Data Stream Classification on solar-flare
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Clustering on solar-flare
0 runs
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Clustering on solar-flare
0 runs - estimation_procedure: 50 times Clustering
Define a new task