Nothing

```
#' Create explainer from your mlr 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, which is one of the most popular one is the mlr package. We would like to present dedicated explain function for it.
#'
#'
#' @inheritParams DALEX::explain
#'
#' @return explainer object (\code{\link[DALEX]{explain}}) ready to work with DALEX
#'
#' @import DALEX
#' @importFrom stats predict
#' @importFrom DALEX yhat
#'
#' @rdname explain_mlr
#' @export
#' @examples
#' library("DALEXtra")
#' titanic_test <- read.csv(system.file("extdata", "titanic_test.csv", package = "DALEXtra"))
#' titanic_train <- read.csv(system.file("extdata", "titanic_train.csv", package = "DALEXtra"))
#' library("mlr")
#' task <- mlr::makeClassifTask(
#' id = "R",
#' data = titanic_train,
#' target = "survived"
#' )
#' learner <- mlr::makeLearner(
#' "classif.gbm",
#' par.vals = list(
#' distribution = "bernoulli",
#' n.trees = 500,
#' interaction.depth = 4,
#' n.minobsinnode = 12,
#' shrinkage = 0.001,
#' bag.fraction = 0.5,
#' train.fraction = 1
#' ),
#' predict.type = "prob"
#' )
#' gbm <- mlr::train(learner, task)
#' explain_mlr(gbm, titanic_test[,1:17], titanic_test[,18])
#'
explain_mlr <-
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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