Solar Flare
Donated on 2/28/1989
Each class attribute counts the number of solar flares of a certain class that occur in a 24 hour period
Dataset Characteristics
Multivariate
Subject Area
Physics and Chemistry
Associated Tasks
Regression
Feature Type
Categorical
# Instances
1389
# Features
10
Dataset Information
Additional Information
Notes: -- 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.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| modified Zurich class | Feature | Categorical | A,B,C,D,E,F,H | no | |
| largest spot size | Feature | Categorical | X,R,S,A,H,K | no | |
| spot distribution | Feature | Categorical | X,O,I,C | no | |
| activity | Feature | Integer | 1 = reduced, 2 = unchanged | no | |
| evolution | Feature | Integer | 1 = decay, 2 = no growth, 3 = growth | no | |
| previous 24 hour flare activity | Feature | Integer | 1 = nothing as big as an M1, 2 = one M1, 3 = more activity than one M1 | no | |
| historically-complex | Feature | Integer | 1 = Yes, 2 = No | no | |
| became complex on this pass | Feature | Integer | . Did region become historically complex on this pass across the sun's disk (1 = yes, 2 = no) | no | |
| area | Feature | Integer | 1 = small, 2 = large | no | |
| area of largest spot | Feature | Integer | 1 = <=5, 2 = >5 | no |
0 to 10 of 13
Additional Variable 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 on this pass across the sun's disk (1 = yes, 2 = no) 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 in the following 24 hours (common flares); Number 12. M-class flares production by this region in the following 24 hours (moderate flares); Number 13. X-class flares production by this region in the following 24 hours (severe flares); Number
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset solar_flare = fetch_ucirepo(id=89) # data (as pandas dataframes) X = solar_flare.data.features y = solar_flare.data.targets # metadata print(solar_flare.metadata) # variable information print(solar_flare.variables)
Solar Flare. (1989). UCI Machine Learning Repository. https://doi.org/10.24432/C5530G.
@misc{misc_solar_flare_89,
title = {{Solar Flare}},
year = {1989},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5530G}
}DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.