Iterative optimization of neural network — ames_mlp

This object has the results when a neural network was tuned using Bayesian optimization and a validation set.

Value

An object with primary class iteration_results.

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)
    )