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Show best desirability scores, based on number of predictors (plural)

Source: R/desirability2.R
show_best_desirability_num.Rd

Similar to show_best_desirability_prop() that can simultaneously optimize multiple scores using desirability functions. See show_best_score_num() for singular scoring method.

Usage

show_best_desirability_num(x, ..., num_terms = 5)

Arguments

x

A tibble or data frame returned by fill_safe_values().

...

One or more desirability selectors to configure the optimization.

num_terms

An integer value specifying the number of predictors to consider.

Value

A tibble with num_terms number of rows. When showing the results, the metrics are presented in "wide format" (one column per metric) and there are new columns for the corresponding desirability values (each starts with .d_).

Details

See show_best_desirability_prop() for details.

Examples


library(desirability2)
library(dplyr)

# Remove outcome
ames_scores_results <- ames_scores_results |>
  dplyr::select(-outcome)
ames_scores_results
#> # A tibble: 5 × 5
#>   predictor    aov_pval cor_pearson    imp_rf infogain
#>   <chr>           <dbl>       <dbl>     <dbl>    <dbl>
#> 1 MS_SubClass    237.         1     0.0148     0.266  
#> 2 MS_Zoning      130.         1     0.00997    0.113  
#> 3 Lot_Frontage   Inf          0.165 0.00668    0.146  
#> 4 Lot_Area       Inf          0.255 0.0137     0.140  
#> 5 Street           5.75       1     0.0000455  0.00365

show_best_desirability_num(
  ames_scores_results,
  maximize(cor_pearson, low = 0, high = 1)
)
#> # A tibble: 5 × 7
#>   predictor    aov_pval cor_pearson  imp_rf infogain .d_max_cor_pearson
#>   <chr>           <dbl>       <dbl>   <dbl>    <dbl>              <dbl>
#> 1 MS_SubClass    237.         1     1.48e-2  0.266                1    
#> 2 MS_Zoning      130.         1     9.97e-3  0.113                1    
#> 3 Street           5.75       1     4.55e-5  0.00365              1    
#> 4 Lot_Area       Inf          0.255 1.37e-2  0.140                0.255
#> 5 Lot_Frontage   Inf          0.165 6.68e-3  0.146                0.165
#> # ℹ 1 more variable: .d_overall <dbl>

show_best_desirability_num(
  ames_scores_results,
  maximize(cor_pearson, low = 0, high = 1),
  maximize(imp_rf)
)
#> # A tibble: 5 × 8
#>   predictor    aov_pval cor_pearson  imp_rf infogain .d_max_cor_pearson
#>   <chr>           <dbl>       <dbl>   <dbl>    <dbl>              <dbl>
#> 1 MS_SubClass    237.         1     1.48e-2  0.266                1    
#> 2 MS_Zoning      130.         1     9.97e-3  0.113                1    
#> 3 Lot_Area       Inf          0.255 1.37e-2  0.140                0.255
#> 4 Lot_Frontage   Inf          0.165 6.68e-3  0.146                0.165
#> 5 Street           5.75       1     4.55e-5  0.00365              1    
#> # ℹ 2 more variables: .d_max_imp_rf <dbl>, .d_overall <dbl>

show_best_desirability_num(
  ames_scores_results,
  maximize(cor_pearson, low = 0, high = 1),
  maximize(imp_rf),
  maximize(infogain)
)
#> # A tibble: 5 × 9
#>   predictor    aov_pval cor_pearson  imp_rf infogain .d_max_cor_pearson
#>   <chr>           <dbl>       <dbl>   <dbl>    <dbl>              <dbl>
#> 1 MS_SubClass    237.         1     1.48e-2  0.266                1    
#> 2 MS_Zoning      130.         1     9.97e-3  0.113                1    
#> 3 Lot_Area       Inf          0.255 1.37e-2  0.140                0.255
#> 4 Lot_Frontage   Inf          0.165 6.68e-3  0.146                0.165
#> 5 Street           5.75       1     4.55e-5  0.00365              1    
#> # ℹ 3 more variables: .d_max_imp_rf <dbl>, .d_max_infogain <dbl>,
#> #   .d_overall <dbl>

show_best_desirability_num(
  ames_scores_results,
  maximize(cor_pearson, low = 0, high = 1),
  maximize(imp_rf),
  maximize(infogain),
  num_terms = 2
)
#> # A tibble: 2 × 9
#>   predictor   aov_pval cor_pearson  imp_rf infogain .d_max_cor_pearson
#>   <chr>          <dbl>       <dbl>   <dbl>    <dbl>              <dbl>
#> 1 MS_SubClass     237.           1 0.0148     0.266                  1
#> 2 MS_Zoning       130.           1 0.00997    0.113                  1
#> # ℹ 3 more variables: .d_max_imp_rf <dbl>, .d_max_infogain <dbl>,
#> #   .d_overall <dbl>

show_best_desirability_num(
  ames_scores_results,
  target(cor_pearson, low = 0.2, target = 0.255, high = 0.9)
)
#> # A tibble: 5 × 7
#>   predictor aov_pval cor_pearson  imp_rf infogain .d_target_cor_pearson
#>   <chr>        <dbl>       <dbl>   <dbl>    <dbl>                 <dbl>
#> 1 Lot_Area    Inf          0.255 1.37e-2  0.140                   1.000
#> 2 MS_SubCl…   237.         1     1.48e-2  0.266                   0    
#> 3 MS_Zoning   130.         1     9.97e-3  0.113                   0    
#> 4 Lot_Fron…   Inf          0.165 6.68e-3  0.146                   0    
#> 5 Street        5.75       1     4.55e-5  0.00365                 0    
#> # ℹ 1 more variable: .d_overall <dbl>

show_best_desirability_num(
  ames_scores_results,
  constrain(cor_pearson, low = 0.2, high = 1)
)
#> # A tibble: 5 × 7
#>   predictor    aov_pval cor_pearson  imp_rf infogain .d_box_cor_pearson
#>   <chr>           <dbl>       <dbl>   <dbl>    <dbl>              <dbl>
#> 1 MS_SubClass    237.         1     1.48e-2  0.266                    1
#> 2 MS_Zoning      130.         1     9.97e-3  0.113                    1
#> 3 Lot_Area       Inf          0.255 1.37e-2  0.140                    1
#> 4 Street           5.75       1     4.55e-5  0.00365                  1
#> 5 Lot_Frontage   Inf          0.165 6.68e-3  0.146                    0
#> # ℹ 1 more variable: .d_overall <dbl>