# NOT RUN {
library(dplyr)
data(ames, package = "modeldata")
ames <-
ames %>%
mutate(Sale_Price = log10(ames$Sale_Price),
Gr_Liv_Area = log10(ames$Gr_Liv_Area))
# ------------------------------------------------------------------------------
# }
# NOT RUN {
cb_fit <-
cubist_rules(committees = 10) %>%
set_engine("Cubist") %>%
fit(Sale_Price ~ Neighborhood + Longitude + Latitude + Gr_Liv_Area + Central_Air,
data = ames)
cb_res <- tidy(cb_fit)
cb_res
cb_res$estimate[[1]]
cb_res$statistic[[1]]
# }
# NOT RUN {
# ------------------------------------------------------------------------------
# }
# NOT RUN {
library(recipes)
xrf_reg_mod <-
rule_fit(trees = 10, penalty = .001) %>%
set_engine("xrf") %>%
set_mode("regression")
# Make dummy variables since xgboost will not
ames_rec <-
recipe(Sale_Price ~ Neighborhood + Longitude + Latitude +
Gr_Liv_Area + Central_Air,
data = ames) %>%
step_dummy(Neighborhood, Central_Air) %>%
step_zv(all_predictors())
ames_processed <- prep(ames_rec) %>% bake(new_data = NULL)
set.seed(1)
xrf_reg_fit <-
xrf_reg_mod %>%
fit(Sale_Price ~ ., data = ames_processed)
xrf_rule_res <- tidy(xrf_reg_fit)
xrf_rule_res$rule[nrow(xrf_rule_res)] %>% rlang::parse_expr()
xrf_col_res <- tidy(xrf_reg_fit, unit = "columns")
xrf_col_res
# }
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