Description Usage Format Details Source References Examples
The data was generated to simulate registration of high energy gamma particles in a Major Atmospheric Gamma-Ray Imaging Cherenkov (MAGIC) Gamma Telescope. The task is to distinguish gamma rays (signal) from hadronic showers (background).
1 | data("MAGICGammaTelescope")
|
A data frame containing 19,020 observations on 11 variables.
major axis of ellipse [mm].
minor axis of ellipse [mm].
10-log of sum of content of all pixels [in #phot].
ratio of sum of two highest pixels over fSize [ratio].
ratio of highest pixel over fSize [ratio].
distance from highest pixel to center, projected onto major axis [mm].
3rd root of third moment along major axis [mm].
3rd root of third moment along minor axis [mm].
angle of major axis with vector to origin [deg].
distance from origin to center of ellipse [mm].
binary variable class, with levels gamma
(signal) and hadron
(background).
Classifying a background event as signal is worse than classifying a signal event as background. For a meaningful comparison of different classifiers the use of an ROC curve with thresholds 0.01, 0.02, 0.05, 0.1, 0.2 is suggested.
The original data was provided by:
R. K. Bock, Major Atmospheric Gamma Imaging Cherenkov Telescope project (MAGIC), rkb '@' mail.cern.ch, https://magic.mppmu.mpg.de/
and was donated by:
P. Savicky, Institute of Computer Science, AS of CR, Czech Republic, savicky '@' cs.cas.cz
The dataset has been taken from the UCI Repository Of Machine Learning Databases at
http://archive.ics.uci.edu/ml/.
Bock, R.K., Chilingarian, A., Gaug, M., Hakl, F., Hengstebeck, T., Jirina, M., Klaschka, J., Kotrc, E., Savicky, P., Towers, S., Vaicilius, A., Wittek W. (2004). Methods for Multidimensional event Classification: a Case Study Using Images From a Cherenkov Gamma-Ray Telescope. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 516(1), 511–528.
P. Savicky, E. Kotrc (2004). Experimental Study of Leaf Confidences for Random Forest. In Proceedings of COMPSTAT, pp. 1767–1774. Physica Verlag, Heidelberg, Germany.
J. Dvorak, P. Savicky (2007). Softening Splits in Decision Trees Using Simulated Annealing. In Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, Part I, pp. 721–729, Springer-Verlag, New-York.
1 2 3 4 5 6 7 8 9 10 11 | data("MAGICGammaTelescope")
summary(MAGICGammaTelescope)
## Not run:
suppressWarnings(RNGversion("3.5.0"))
set.seed(1090)
mgtt <- evtree(class ~ . , data = MAGICGammaTelescope)
mgtt
table(predict(mgtt), MAGICGammaTelescope$class)
plot(mgtt)
## End(Not run)
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