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solar-flare

solar-flare

active ARFF public Visibility: public Uploaded 06-04-2017 by Arnold
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Author: Gary Bradshaw Source: [UCI](http://archive.ics.uci.edu/ml/datasets/solar+flare) Please cite: Solar Flare database 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. Number of Instances: flare.data1: 323, flare.data2: 1066 Number of attributes: 13 (includes 3 class attributes) ### 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) CLASSTYPE: nominal CLASSINDEX: first

13 features

class (target)nominal6 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
X-class_flares_production_by_this_regionnominal3 unique values
0 missing
Show all 13 features

62 properties

NumberOfInstances
1066
Number of instances (rows) of the dataset.
NumberOfFeatures
13
Number of attributes (columns) of the dataset.
NumberOfClasses
6
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.
Quartile1MutualInformation
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
30.77
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.19
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
2.14
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0.58
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.09
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.22
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.22
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.22
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
ClassEntropy
2.36
Entropy of the target attribute values.
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
10.53
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
31.05
Percentage of instances belonging to the most frequent class.
MajorityClassSize
331
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
2.19
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MaxMutualInformation
1.08
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
8
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
0.75
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
4
Number of binary attributes.
MeanMutualInformation
0.22
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
2.33
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
3.69
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
-0
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
1
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
4.03
Percentage of instances belonging to the least frequent class.
MinorityClassSize
43
Number of instances belonging to the least frequent class.
Show all 62 properties

22 tasks

Supervised Classification on solar-flare
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on solar-flare
0 runs - estimation_procedure: 4-fold Crossvalidation - 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
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
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 - 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
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