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#' Create explainer from your xgboost model
#'
#' DALEX is designed to work with various black-box models like tree ensembles, linear models, neural networks etc.
#' Unfortunately R packages that create such models are very inconsistent. Different tools use different interfaces to train, validate and use models.
#' One of those tools, we would like to make more accessible is the xgboost package.
#'
#'
#' @inheritParams DALEX::explain
#'
#' @return explainer object (\code{\link[DALEX]{explain}}) ready to work with DALEX
#'
#' @import DALEX
#' @importFrom DALEX yhat
#'
#'
#' @examples
#' \donttest{
#' library("xgboost")
#' library("DALEXtra")
#' # 8th column is target that has to be omitted in X data
#' data <- titanic_imputed[,-8]
#' y <- titanic_imputed$survived
#' model <- xgboost(data, as.factor(y), nrounds = 10,
#' objective = "binary:logistic", nthreads = 1)
#'
#' explainer_1 <- explain_xgboost(model, data = titanic_imputed[,-8],
#' titanic_imputed$survived)
#' plot(predict_parts(explainer_1, titanic_imputed[1,-8]))
#' }
#'
#' @rdname explain_xgboost
#' @export
explain_xgboost <-
function(model,
data = NULL,
y = NULL,
weights = NULL,
predict_function = NULL,
predict_function_target_column = NULL,
residual_function = NULL,
...,
label = NULL,
verbose = TRUE,
precalculate = TRUE,
colorize = !isTRUE(getOption('knitr.in.progress')),
model_info = NULL,
type = NULL) {
explain(
model,
data = data,
y = y,
weights = weights,
predict_function = predict_function,
predict_function_target_column = predict_function_target_column,
residual_function = residual_function,
...,
label = label,
verbose = verbose,
precalculate = precalculate,
colorize = colorize,
model_info = model_info,
type = type
)
}
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