Nothing
#' Tuned flexible discriminant analysis results
#'
#' This object has the results when a flexible discriminant analysis model was
#' tuned over the interaction degree parameters.
#'
#' @details To reduce the object size, five bootstraps were used for resampling
#' and missing data were removed.
#'
#' The code used to produce this object:
#'
#' ```
#' library(tidymodels)
#' library(discrim)
#' tidymodels_prefer()
#'
#' # ------------------------------------------------------------------------------
#'
#' ctrl_gr <- control_grid(save_pred = TRUE)
#'
#' # ------------------------------------------------------------------------------
#'
#' data(scat)
#' scat <- scat[complete.cases(scat), ]
#'
#' # ------------------------------------------------------------------------------
#'
#' set.seed(1)
#' scat_rs <- bootstraps(scat, times = 5)
#'
#' scat_fda_bt <-
#' discrim_flexible(prod_degree = tune()) %>%
#' tune_grid(
#' Species ~ .,
#' resamples = scat_rs,
#' control = ctrl_gr
#' )
#' ```
#'
#' @aliases scat_fda_bt
#' @name scat_fda_bt
#' @docType data
#' @return An object with primary class `tune_results`.
#' @keywords datasets
NULL
#' Resampled bagged tree results
#'
#' This object has the results when a bagged regression tree was resampled using
#' 10-fold cross-validation.
#'
#' @details The code used to produce this object:
#'
#' ```
#' library(tidymodels)
#' library(baguette)
#' tidymodels_prefer()
#'
#' # ------------------------------------------------------------------------------
#'
#' ctrl_rs <- control_resamples(save_pred = TRUE)
#'
#' # ------------------------------------------------------------------------------
#'
#' set.seed(1)
#' cars_rs <- vfold_cv(mtcars)
#'
#' cars_bag_vfld <-
#' bag_tree() %>%
#' set_engine("rpart", times = 5) %>%
#' set_mode("regression") %>%
#' fit_resamples(
#' mpg ~ .,
#' resamples = cars_rs,
#' control = ctrl_rs
#' )
#' ```
#'
#' @aliases cars_bag_vfld
#' @name cars_bag_vfld
#' @docType data
#' @return An object with primary class `resample_results`.
#' @keywords datasets
NULL
#' Test set results for logistic regression
#'
#' This object has the results when a logistic regression model is fit to the
#' training set and is evaluated on the test set.
#'
#' @details The code used to produce this object:
#'
#' ```
#' library(tidymodels)
#' tidymodels_prefer()
#'
#' # ------------------------------------------------------------------------------
#'
#' set.seed(1)
#' data(two_class_dat)
#'
#' # ------------------------------------------------------------------------------
#'
#' two_class_split <- initial_split(two_class_dat)
#' # ------------------------------------------------------------------------------
#'
#' glm_spec <- logistic_reg()
#'
#' two_class_final <-
#' glm_spec %>%
#' last_fit(
#' Class ~ .,
#' split = two_class_split
#' )
#' ```
#'
#' @aliases two_class_final
#' @name two_class_final
#' @docType data
#' @return An object with primary class `last_fit`.
#' @keywords datasets
NULL
#' A CART classification tree tuned via racing
#'
#' This object has the results when a CART classification tree model was
#' tuned over the cost-complexity parameter using racing.
#'
#' @details To reduce the object size, a smaller subset of the data were used.
#'
#' The code used to produce this object:
#'
#' ```
#' library(tidymodels)
#' library(finetune)
#' tidymodels_prefer()
#'
#' ctrl_rc <- control_race(save_pred = TRUE)
#'
#' # ------------------------------------------------------------------------------
#'
#' data(cells)
#'
#' set.seed(1)
#' cells <-
#' cells %>%
#' select(-case) %>%
#' sample_n(200)
#'
#' # ------------------------------------------------------------------------------
#'
#' set.seed(2)
#' cell_rs <- vfold_cv(cells)
#'
#' # ------------------------------------------------------------------------------
#'
#' set.seed(3)
#' cell_race <-
#' decision_tree(cost_complexity = tune()) %>%
#' set_mode("classification") %>%
#' tune_race_anova(
#' class ~ .,
#' resamples = cell_rs,
#' grid = tibble(cost_complexity = 10^seq(-2, -1, by = 0.2)),
#' control = ctrl_rc
#' )
#' ```
#'
#' @aliases cell_race
#' @name cell_race
#' @docType data
#' @return An object with primary class `tune_race`.
#' @keywords datasets
NULL
#' Iterative optimization of neural network
#'
#' This object has the results when a neural network was tuned using Bayesian
#' optimization and a validation set.
#'
#' @details The code used to produce this object:
#'
#' ```
#' data(ames)
#'
#' ames <-
#' ames %>%
#' select(Sale_Price, Neighborhood, Longitude, Latitude, Year_Built) %>%
#' mutate(Sale_Price = log10(ames$Sale_Price))
#'
#' set.seed(1)
#' ames_rs <- validation_split(ames)
#'
#' ames_rec <-
#' recipe(Sale_Price ~ ., data = ames) %>%
#' step_dummy(all_nominal_predictors()) %>%
#' step_zv(all_predictors()) %>%
#' step_normalize(all_predictors())
#'
#' mlp_spec <-
#' mlp(hidden_units = tune(),
#' penalty = tune(),
#' epochs = tune()) %>%
#' set_mode("regression")
#'
#' set.seed(1)
#' ames_mlp_itr <-
#' mlp_spec %>%
#' tune_bayes(
#' ames_rec,
#' resamples = ames_rs,
#' initial = 5,
#' iter = 4,
#' control = control_bayes(save_pred = TRUE)
#' )
#' ```
#'
#' @aliases ames_mlp_itr
#' @name ames_mlp_itr
#' @docType data
#' @return An object with primary class `iteration_results`.
#' @keywords datasets
NULL
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