R tune show_best 研究最佳调整参数

show_best(x, ...)

# S3 method for default
show_best(x, ...)

# S3 method for tune_results
show_best(x, metric = NULL, n = 5, ...)

select_best(x, ...)

# S3 method for default
select_best(x, ...)

# S3 method for tune_results
select_best(x, metric = NULL, ...)

select_by_pct_loss(x, ...)

# S3 method for default
select_by_pct_loss(x, ...)

# S3 method for tune_results
select_by_pct_loss(x, ..., metric = NULL, limit = 2)

select_by_one_std_err(x, ...)

# S3 method for default
select_by_one_std_err(x, ...)

# S3 method for tune_results
select_by_one_std_err(x, ..., metric = NULL)

data("example_ames_knn")

show_best(ames_iter_search, metric = "rmse")
#> # A tibble: 5 × 12
#>       K weight_func dist_power   lon   lat .metric .estimator   mean     n
#>   <int> <chr>            <dbl> <int> <int> <chr>   <chr>       <dbl> <int>
#> 1    33 triweight        0.511    10     3 rmse    standard   0.0728    10
#> 2     5 rank             0.411     2     7 rmse    standard   0.0740    10
#> 3    21 triweight        0.909    10     4 rmse    standard   0.0742    10
#> 4    21 cos              0.626     1     4 rmse    standard   0.0746    10
#> 5    19 inv              0.117     1     4 rmse    standard   0.0758    10
#> # ℹ 3 more variables: std_err <dbl>, .config <chr>, .iter <int>

select_best(ames_iter_search, metric = "rsq")
#> # A tibble: 1 × 6
#>       K weight_func dist_power   lon   lat .config              
#>   <int> <chr>            <dbl> <int> <int> <chr>                
#> 1    33 triweight        0.511    10     3 Preprocessor10_Model1

# To find the least complex model within one std error of the numerically
# optimal model, the number of nearest neighbors are sorted from the largest
# number of neighbors (the least complex class boundary) to the smallest
# (corresponding to the most complex model).

select_by_one_std_err(ames_grid_search, metric = "rmse", desc(K))
#> # A tibble: 1 × 13
#>       K weight_func dist_power   lon   lat .metric .estimator   mean     n
#>   <int> <chr>            <dbl> <int> <int> <chr>   <chr>       <dbl> <int>
#> 1    33 triweight        0.511    10     3 rmse    standard   0.0728    10
#> # ℹ 4 more variables: std_err <dbl>, .config <chr>, .best <dbl>,
#> #   .bound <dbl>

# Now find the least complex model that has no more than a 5% loss of RMSE:
select_by_pct_loss(
  ames_grid_search,
  metric = "rmse",
  limit = 5, desc(K)
)
#> # A tibble: 1 × 13
#>       K weight_func dist_power   lon   lat .metric .estimator   mean     n
#>   <int> <chr>            <dbl> <int> <int> <chr>   <chr>       <dbl> <int>
#> 1    33 triweight        0.511    10     3 rmse    standard   0.0728    10
#> # ℹ 4 more variables: std_err <dbl>, .config <chr>, .best <dbl>,
#> #   .loss <dbl>