MAGICGammaTelescope {evtree} | R Documentation |
MAGIC Gamma Telescope
Description
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).
Usage
data("MAGICGammaTelescope")
Format
A data frame containing 19,020 observations on 11 variables.
- fLength
major axis of ellipse [mm].
- fWidth
minor axis of ellipse [mm].
- fSize
10-log of sum of content of all pixels [in #phot].
- fConc
ratio of sum of two highest pixels over fSize [ratio].
- fConc1
ratio of highest pixel over fSize [ratio].
- fAsym
distance from highest pixel to center, projected onto major axis [mm].
- fM3Long
3rd root of third moment along major axis [mm].
- fM3Trans
3rd root of third moment along minor axis [mm].
- fAlpha
angle of major axis with vector to origin [deg].
- fDist
distance from origin to center of ellipse [mm].
- class
binary variable class, with levels
gamma
(signal) andhadron
(background).
Details
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.
Source
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/.
References
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.
Examples
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)