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)
<- validation_split(ames)
ames_rs
<-
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)
)