Nothing

```
#' 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
#' @param encode_function function(data, ...) that if executed with \code{data}
#' parameters returns encoded dataframe that was used to fit model. Xgboost does
#' not handle factors on it's own so such function is needed to acquire better explanations.
#' @param true_labels a vector of \code{y} before encoding.
#'
#' @return explainer object (\code{\link[DALEX]{explain}}) ready to work with DALEX
#'
#' @import DALEX
#' @importFrom DALEX yhat
#'
#'
#' @examples
#' library("xgboost")
#' library("DALEXtra")
#' library("mlr")
#' # 8th column is target that has to be omitted in X data
#' data <- as.matrix(createDummyFeatures(titanic_imputed[,-8]))
#' model <- xgboost(data, titanic_imputed$survived, nrounds = 10,
#' params = list(objective = "binary:logistic"),
#' prediction = TRUE)
#' # explainer with encode functiom
#' explainer_1 <- explain_xgboost(model, data = titanic_imputed[,-8],
#' titanic_imputed$survived,
#' encode_function = function(data) {
#' as.matrix(createDummyFeatures(data))
#' })
#' plot(predict_parts(explainer_1, titanic_imputed[1,-8]))
#'
#' # explainer without encode function
#' explainer_2 <- explain_xgboost(model, data = data, titanic_imputed$survived)
#' plot(predict_parts(explainer_2, data[1,,drop = FALSE]))
#'
#' @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,
encode_function = NULL,
true_labels = NULL) {
attr(model, "encoder") <- encode_function
if (!is.null(true_labels)) {
attr(model, "true_labels") <- true_labels
y <- true_labels
}
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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