#' Haberman survival data set
#'
#' @description Training data for the Haberman dataset.
#'
#' @details This data set contains cases from a study that was conducted between 1958 and 1970
#' at the University of Chicago's Billings Hospital on the survival of patients who had undergone
#' surgery for breast cancer. The task is to determine if the patient survived 5 years or longer
#' (positive) or if the patient died within 5 year (negative)
#'
#' @format A SDEFSR_Dataset class with 306 instances, 3 variables (without the target variable) and 2 values for the target variable.
#' Three fuzzy labels for each numerical variable are defined.
#'
#' @source Haberman, S. J. (1976). Generalized Residuals for Log-Linear Models,
#' Proceedings of the 9th International Biometrics Conference, Boston, pp. 104-122.
#' @source Landwehr, J. M., Pregibon, D., and Shoemaker, A. C. (1984), Graphical Models for
#' Assessing Logistic Regression Models (with discussion), Journal of the American Statistical
#' Association 79: 61-83.
#' @source Lo, W.-D. (1993). Logistic Regression Trees, PhD thesis, Department of Statistics,
#' University of Wisconsin, Madison, WI.
#'
#' @examples
#' habermanTra$data
#' habermanTra$fuzzySets
#'
#' @docType data
#' @name habermanTra
NULL
#' Haberman survival data set
#'
#' @description Test data for the Haberman dataset.
#'
#' @details This data set contains cases from a study that was conducted between 1958 and 1970
#' at the University of Chicago's Billings Hospital on the survival of patients who had undergone
#' surgery for breast cancer. The task is to determine if the patient survived 5 years or longer
#' (positive) or if the patient died within 5 year (negative)
#'
#' @format A SDEFSR_Dataset class with 62 instances, 3 variables (without the target variable) and 2 values for the target variable.
#' Three fuzzy labels for each numerical variable are defined.
#'
#' @source Haberman, S. J. (1976). Generalized Residuals for Log-Linear Models,
#' Proceedings of the 9th International Biometrics Conference, Boston, pp. 104-122.
#' @source Landwehr, J. M., Pregibon, D., and Shoemaker, A. C. (1984), Graphical Models for
#' Assessing Logistic Regression Models (with discussion), Journal of the American Statistical
#' Association 79: 61-83.
#' @source Lo, W.-D. (1993). Logistic Regression Trees, PhD thesis, Department of Statistics,
#' University of Wisconsin, Madison, WI.
#'
#' @examples
#' habermanTra$data
#' habermanTra$fuzzySets
#' @docType data
#' @name habermanTst
NULL
#' Haberman survival rule set
#'
#' @description Rules generated by the SDIGA algorithm with the default parameters for the \code{haberman} dataset.
#'
#' @details The rule set contains only two rules. One for each target variable
#'
#'
#' @source Haberman, S. J. (1976). Generalized Residuals for Log-Linear Models,
#' Proceedings of the 9th International Biometrics Conference, Boston, pp. 104-122.
#' @source Landwehr, J. M., Pregibon, D., and Shoemaker, A. C. (1984), Graphical Models for
#' Assessing Logistic Regression Models (with discussion), Journal of the American Statistical
#' Association 79: 61-83.
#' @source Lo, W.-D. (1993). Logistic Regression Trees, PhD thesis, Department of Statistics,
#' University of Wisconsin, Madison, WI.
#'
#' @examples
#' habermanRules
#' @docType data
#' @name habermanRules
NULL
#' Car evaluation dataset
#'
#' @description Training data for the car dataset
#'
#' @details Car Evaluation Database was derived from a simple hierarchical decision model.
#' The model evaluates cars according to six input attributes: buying, maint, doors,
#' persons, lug_boot, safety.
#'
#' @format A SDEFSR_Dataset class with 1382 instances, 6 variables (without the target variable) and 4 values for the target Variable.
#' Three labels for each variable are defined.
#'
#' @source M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute
#' decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon,
#' France. pages 59-78, 1988.
#'
#' @source B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by function decomposition.
#' ICML-97, Nashville, TN. 1997 (to appear).
#'
#' @examples
#' carTra$data
#' carTra$atributeNames
#'
#' @docType data
#' @name carTra
NULL
#' Car evaluation dataset
#'
#' @description Test data for the car dataset
#'
#' @details Car Evaluation Database was derived from a simple hierarchical decision model.
#' The model evaluates cars according to six input attributes: buying, maint, doors,
#' persons, lug_boot, safety.
#'
#' @format A SDEFSR_Dataset class with 346 instances, 6 variables (without the target variable) and 4 values for the target Variable.
#' Three labels for each variable are defined.
#'
#' @source M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute
#' decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon,
#' France. pages 59-78, 1988.
#'
#' @source B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by function decomposition.
#' ICML-97, Nashville, TN. 1997 (to appear).
#'
#' @examples
#' carTra$data
#' carTra$atributeNames
#'
#' @docType data
#' @name carTst
NULL
#' German Credit data set
#'
#' @description Training data for the german dataset
#'
#' @details A numerical version of the Statlog German Credit Data data set.
#' Here, the task is to clasify customers as good (1) or bad (2),
#' depending on 20 features about them and their bancary accounts.
#'
#' @format A SDEFSR_Dataset class with 800 instances, 20 variables (without the target variable)
#' and 2 values for the target class.
#'
#' @source \url{http://sci2s.ugr.es/keel/dataset.php?cod=88}
#'
#' @examples
#' germanTra$data
#'
#' @docType data
#' @name germanTra
NULL
#' German Credit data set
#'
#' @description Test data for the german dataset
#'
#' @details A numerical version of the Statlog German Credit Data data set.
#' Here, the task is to clasify customers as good (1) or bad (2),
#' depending on 20 features about them and their bancary accounts.
#'
#' @format A SDEFSR_Dataset class with 200 instances, 20 variables (without the target variable)
#' and 2 values for the target class.
#'
#' @source \url{http://sci2s.ugr.es/keel/dataset.php?cod=88}
#'
#' @examples
#' germanTra$data
#'
#' @docType data
#' @name germanTst
NULL
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