Data
solar-flare

solar-flare

deactivated ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
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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

107 properties

1066
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
0
Number of numeric attributes.
13
Number of nominal attributes.
0
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.05
Entropy of the target attribute values.
-0
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1061
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.36
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
1
The minimal number of distinct values among attributes of the nominal type.
30.77
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
0.02
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.58
Third quartile of entropy among attributes.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
4.95
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
8
The maximum number of distinct values among attributes of the nominal type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
0.09
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
1
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.94
Average entropy of the attributes.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.25
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
-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
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.02
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Average mutual information between the nominal attributes and the target attribute.
4
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-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
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
99.23
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
2.14
Standard deviation of the number of distinct values among attributes of the nominal type.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.69
Average number of distinct values among the attributes of the nominal type.
First quartile of standard deviation of attributes of the numeric type.
0.45
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
-0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
Mean standard deviation of attributes of the numeric type.
0.78
Second quartile (Median) of entropy among attributes.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-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
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
99.53
Percentage of instances belonging to the most frequent class.

19 tasks

1232 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: X-class_flares_production_by_this_region
311 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: X-class_flares_production_by_this_region
191 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: X-class_flares_production_by_this_region
176 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
300 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: X-class_flares_production_by_this_region
99 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: X-class_flares_production_by_this_region
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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