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solar-flare

solar-flare

active ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
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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

62 properties

1066
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
6
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.
0.01
Number of attributes divided by the number of instances.
3.69
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
10.53
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
31.05
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
0.09
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
331
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
2.19
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
30.77
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
1.22
Third quartile of entropy among attributes.
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
1.08
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
8
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
100
Percentage of nominal attributes.
0.22
Third quartile of mutual information between the nominal attributes and the target attribute.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0.19
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
4.03
Percentage of instances belonging to the least frequent class.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.75
Average entropy of the attributes.
43
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
2.14
Standard deviation of the number of distinct values among attributes of the nominal type.
Mean kurtosis among attributes of the numeric type.
4
Number of binary attributes.
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
Mean of means among attributes of the numeric type.
First quartile of skewness among attributes of the numeric type.
0.22
Average class difference between consecutive instances.
0.22
Average mutual information between the nominal attributes and the target attribute.
First quartile of standard deviation of attributes of the numeric type.
2.36
Entropy of the target attribute values.
2.33
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.58
Second quartile (Median) of entropy among attributes.

11 tasks

31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - 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
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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