Nothing

```
#' Create explainer from your tidymodels workflow.
#'
#' 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 tidymodels 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 DALEX yhat
#'
#' @rdname explain_tidymodels
#' @export
#' @examples
#' library("DALEXtra")
#' library("tidymodels")
#' library("recipes")
#' data <- titanic_imputed
#' data$survived <- as.factor(data$survived)
#' rec <- recipe(survived ~ ., data = data) %>%
#' step_normalize(fare)
#' model <- decision_tree(tree_depth = 25) %>%
#' set_engine("rpart") %>%
#' set_mode("classification")
#'
#' wflow <- workflow() %>%
#' add_recipe(rec) %>%
#' add_model(model)
#'
#'
#' model_fitted <- wflow %>%
#' fit(data = data)
#'
#' explain_tidymodels(model_fitted, data = titanic_imputed, y = titanic_imputed$survived)
#'
#'
explain_tidymodels <-
function(model,
data = NULL,
y = NULL,
weights = NULL,
predict_function = NULL,
predict_function_target_column = NULL,
residual_function = NULL,
...,
label = NULL,
verbose = TRUE,
precalculate = TRUE,
colorize = !isTRUE(getOption('knitr.in.progress')),
model_info = NULL,
type = NULL) {
if (inherits(model, "workflow") && !model$trained) {
stop("Only trained workflows can be passed to explain function")
}
# for classification models do not calculate residuals (default)
# as this my rise some not needed warnings
if (is.null(residual_function) && isTRUE(model$spec$mode == "classification")) {
residual_function <- function(m, d, y, predict_function) 0
}
if (!is.null(type) && type == "class") {
type = "classification"
predict_function <- function(X.model, newdata, ...)
predict(X.model, newdata, type = "class")
}
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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