R stacks add_candidates 將模型定義添加到數據堆棧

add_candidates(
  data_stack,
  candidates,
  name = deparse(substitute(candidates)),
  ...
)

其他核心動詞：blend_predictions()、fit_members()、stacks()

# see the "Example Data" section above for
# clarification on the objects used in these examples!

# put together a data stack using
# tuning results for regression models
reg_st <- 
  stacks() %>%
  add_candidates(reg_res_lr) %>%
  add_candidates(reg_res_svm) %>%
  add_candidates(reg_res_sp)
  
reg_st
#> # A data stack with 3 model definitions and 16 candidate members:
#> #   reg_res_lr: 1 model configuration
#> #   reg_res_svm: 5 model configurations
#> #   reg_res_sp: 10 model configurations
#> # Outcome: latency (numeric)
  
# do the same with multinomial classification models
class_st <-
  stacks() %>%
  add_candidates(class_res_nn) %>%
  add_candidates(class_res_rf)
#> Warning: Predictions from 1 candidate were identical to those from existing
#> candidates and were removed from the data stack.
  
class_st
#> # A data stack with 2 model definitions and 10.6666666666667 candidate members:
#> #   class_res_nn: 1 model configuration
#> #   class_res_rf: 9.66666666666667 model configurations
#> # Outcome: reflex (factor)
  
# ...or binomial classification models
log_st <-
  stacks() %>%
  add_candidates(log_res_nn) %>%
  add_candidates(log_res_rf)
  
log_st
#> # A data stack with 2 model definitions and 11 candidate members:
#> #   log_res_nn: 1 model configuration
#> #   log_res_rf: 10 model configurations
#> # Outcome: hatched (factor)
  
# use custom names for each model:
log_st2 <-
  stacks() %>%
  add_candidates(log_res_nn, name = "neural_network") %>%
  add_candidates(log_res_rf, name = "random_forest")
  
log_st2
#> # A data stack with 2 model definitions and 11 candidate members:
#> #   neural_network: 1 model configuration
#> #   random_forest: 10 model configurations
#> # Outcome: hatched (factor)
  
# these objects would likely then be
# passed to blend_predictions():
log_st2 %>% blend_predictions()
#> ── A stacked ensemble model ─────────────────────────────────────
#> 
#> Out of 11 possible candidate members, the ensemble retained 2.
#> Penalty: 0.1.
#> Mixture: 1.
#> 
#> The 2 highest weighted member classes are:
#> # A tibble: 2 × 3
#>   member                      type        weight
#>   <chr>                       <chr>        <dbl>
#> 1 .pred_no_neural_network_1_1 mlp           4.99
#> 2 .pred_no_random_forest_1_05 rand_forest   1.35
#> 
#> Members have not yet been fitted with `fit_members()`.