library(tidymodels)
library(tune)
library(AmesHousing)
# ------------------------------------------------------------------------------
ames <- make_ames()
# Make sure that you get the same random numbers
set.seed(4595)
data_split <- initial_split(ames, strata = "Sale_Price")
ames_train <- training(data_split)
set.seed(2453)
cv_splits <- vfold_cv(ames_train, v = 10, strata = "Sale_Price")
# ------------------------------------------------------------------------------
ames_rec <-
recipe(Sale_Price ~ ., data = ames_train) %>%
step_log(Sale_Price, base = 10) %>%
step_YeoJohnson(Lot_Area, Gr_Liv_Area) %>%
step_other(Neighborhood, threshold = .1) %>%
step_dummy(all_nominal()) %>%
step_zv(all_predictors())
svm_model <-
svm_rbf(
mode = "regression", cost = tune(), rbf_sigma = tune()) %>%
set_engine("kernlab")
ames_wflow <-
workflow() %>%
add_recipe(ames_rec) %>%
add_model(svm_model)
set.seed(4567367)
ames_set <-
parameters(ames_wflow)
ames_grid <-
ames_set %>%
grid_max_entropy(size = 3)
initial_grid <- tune_grid(ames_wflow, resamples = cv_splits, grid = ames_grid, control = control_grid(verbose = TRUE))
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