ucimlr: UCI Machine Learning Repository

#' MAGIC Gamma Telescope
#'
#' The data are MC generated (see below) to simulate registration of high energy
#' gamma particles in a ground-based atmospheric Cherenkov gamma telescope using the
#' imaging technique. Cherenkov gamma telescope observes high energy gamma rays,
#' taking advantage of the radiation emitted by charged particles produced
#' inside the electromagnetic showers initiated by the gammas, and developing in the
#' atmosphere. This Cherenkov radiation (of visible to UV wavelengths) leaks
#' through the atmosphere and gets recorded in the detector, allowing reconstruction
#' of the shower parameters. The available information consists of pulses left by
#' the incoming Cherenkov photons on the photomultiplier tubes, arranged in a
#' plane, the camera. Depending on the energy of the primary gamma, a total of
#' few hundreds to some 10000 Cherenkov photons get collected, in patterns
#' (called the shower image), allowing to discriminate statistically those
#' caused by primary gammas (signal) from the images of hadronic showers
#' initiated by cosmic rays in the upper atmosphere (background).
#'
#' @format A data frame with 19020 observations on the following 11 variables.
#' \enumerate{
#'   \item fLength:  continuous  # major axis of ellipse [mm]
#'   \item fWidth:   continuous  # minor axis of ellipse [mm]
#'   \item fSize:    continuous  # 10-log of sum of content of all pixels [in #phot]
#'   \item fConc:    continuous  # ratio of sum of two highest pixels over fSize  [ratio]
#'   \item fConc1:   continuous  # ratio of highest pixel over fSize  [ratio]
#'   \item fAsym:    continuous  # distance from highest pixel to center, projected onto major axis [mm]
#'   \item fM3Long:  continuous  # 3rd root of third moment along major axis  [mm]
#'   \item fM3Trans: continuous  # 3rd root of third moment along minor axis  [mm]
#'   \item fAlpha:   continuous  # angle of major axis with vector to origin [deg]
#'   \item fDist:    continuous  # distance from origin to center of ellipse [mm]
#'   \item class:    g,h         # gamma (signal), hadron (background)
#' }
#'
#' @details
#' Typically, the image of a shower after some pre-processing is an elongated
#' cluster. Its long axis is oriented towards the camera center if the shower axis
#' is parallel to the telescope's optical axis, i.e. if the telescope axis is
#' directed towards a point source. A principal component analysis is performed
#' in the camera plane, which results in a correlation axis and defines an ellipse.
#' If the depositions were distributed as a bivariate Gaussian, this would be
#' an equidensity ellipse. The characteristic parameters of this ellipse
#' (often called Hillas parameters) are among the image parameters that can be
#' used for discrimination. The energy depositions are typically asymmetric
#' along the major axis, and this asymmetry can also be used in discrimination.
#' There are, in addition, further discriminating characteristics, like the
#' extent of the cluster in the image plane, or the total sum of depositions.
#'
#' The data set was generated by a Monte Carlo program, Corsika, described in
#' D. Heck et al., CORSIKA, A Monte Carlo code to simulate extensive air showers,
#' Forschungszentrum Karlsruhe FZKA 6019 (1998).
#'
#' The program was run with parameters allowing to observe events with energies down
#' to below 50 GeV.
#'
#' For technical reasons, the number of h events is underestimated.
#' In the real data, the h class represents the majority of the events.
#'
#' The simple classification accuracy is not meaningful for this data, since
#' classifying a background event as signal is worse than classifying a signal
#' event as background. For comparison of different classifiers an ROC curve
#' has to be used. The relevant points on this curve are those, where the
#' probability of accepting a background event as signal is below one of the
#' following thresholds: 0.01, 0.02, 0.05, 0.1, 0.2 depending on the required
#' quality of the sample of the accepted events for different experiments.
#'
#' @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.
#' Nucl.Instr.Meth. A, 516, pp. 511-528.
#'
#' P. Savicky, E. Kotrc.
#' Experimental Study of Leaf Confidences for Random Forest.
#' Proceedings of COMPSTAT 2004, In: Computational Statistics.
#' (Ed.: Antoch J.) - Heidelberg, Physica Verlag 2004, pp. 1767-1774.
#'
#' J. Dvorak, P. Savicky.
#' Softening Splits in Decision Trees Using Simulated Annealing.
#' Proceedings of ICANNGA 2007, Warsaw, (Ed.: Beliczynski et. al),
#' Part I, LNCS 4431, pp. 721-729.
#'
#' \url{https://archive.ics.uci.edu/ml/machine-learning-databases/{dataset endpoint}}
#'
#' \url{https://archive.ics.uci.edu/ml/datasets/{dataset endpoint}}
#'
#' @source
#' Original owner of the database:
#'
#' R. K. Bock
#' Major Atmospheric Gamma Imaging Cherenkov Telescope project (MAGIC)
#' http://wwwmagic.mppmu.mpg.de
#' rkb@mail.cern.ch
#'
#' Donor:
#'
#' P. Savicky
#' Institute of Computer Science, AS of CR
#' Czech Republic
#' savicky@cs.cas.cz
#'
#' Date received: May 2007
"magic_gamma"