print.md
In stacks: Tidy Model Stacking

data stack printing works

Code
  stacks()
Output
  # A data stack with 0 model definitions and 0 candidate members.

Code
  st_reg_1
Output
  # A data stack with 1 model definition and 5 candidate members:
  #   reg_res_svm: 5 model configurations
  # Outcome: latency (numeric)

Code
  st_class_1
Output
  # A data stack with 1 model definition and 9.66666666666667 candidate members:
  #   class_res_rf: 9.66666666666667 model configurations
  # Outcome: reflex (factor)

Code
  st_log_1
Output
  # A data stack with 1 model definition and 10 candidate members:
  #   log_res_rf: 10 model configurations
  # Outcome: hatched (factor)

model stack printing works

Code
  st_reg_1_
Message
  -- A stacked ensemble model -------------------------------------

  Out of 5 possible candidate members, the ensemble retained 2.
  Penalty: 0.1.
  Mixture: 1.

  The 2 highest weighted members are:
Output
  # A tibble: 2 x 3
    member          type    weight
    <chr>           <chr>    <dbl>
  1 reg_res_svm_1_3 svm_rbf  1.18 
  2 reg_res_svm_1_2 svm_rbf  0.203
Message

  Members have not yet been fitted with `fit_members()`.

Code
  st_class_1_
Message
  -- A stacked ensemble model -------------------------------------

  Out of 19 possible candidate members, the ensemble retained 6.
  Penalty: 0.1.
  Mixture: 1.
  Across the 3 classes, there are an average of 3 coefficients per class.

  The 6 highest weighted member classes are:
Output
  # A tibble: 6 x 4
    member                       type         weight class
    <chr>                        <chr>         <dbl> <fct>
  1 .pred_full_class_res_rf_1_05 rand_forest 3.81    full 
  2 .pred_mid_class_res_rf_1_06  rand_forest 0.674   mid  
  3 .pred_full_class_res_rf_1_07 rand_forest 0.396   full 
  4 .pred_full_class_res_rf_1_01 rand_forest 0.0544  full 
  5 .pred_full_class_res_rf_1_06 rand_forest 0.0155  full 
  6 .pred_full_class_res_rf_1_04 rand_forest 0.00356 full 
Message

  Members have not yet been fitted with `fit_members()`.

Code
  st_log_1_
Message
  -- A stacked ensemble model -------------------------------------

  Out of 10 possible candidate members, the ensemble retained 2.
  Penalty: 0.01.
  Mixture: 1.

  The 2 highest weighted member classes are:
Output
  # A tibble: 2 x 3
    member                   type        weight
    <chr>                    <chr>        <dbl>
  1 .pred_no_log_res_rf_1_02 rand_forest   3.75
  2 .pred_no_log_res_rf_1_05 rand_forest   2.70
Message

  Members have not yet been fitted with `fit_members()`.

Code
  st_reg_1__
Message
  -- A stacked ensemble model -------------------------------------

  Out of 5 possible candidate members, the ensemble retained 2.
  Penalty: 0.1.
  Mixture: 1.

  The 2 highest weighted members are:
Output
  # A tibble: 2 x 3
    member          type    weight
    <chr>           <chr>    <dbl>
  1 reg_res_svm_1_3 svm_rbf  1.18 
  2 reg_res_svm_1_2 svm_rbf  0.203

Code
  st_class_1__
Message
  -- A stacked ensemble model -------------------------------------

  Out of 19 possible candidate members, the ensemble retained 6.
  Penalty: 0.1.
  Mixture: 1.
  Across the 3 classes, there are an average of 3 coefficients per class.

  The 6 highest weighted member classes are:
Output
  # A tibble: 6 x 4
    member                       type         weight class
    <chr>                        <chr>         <dbl> <fct>
  1 .pred_full_class_res_rf_1_05 rand_forest 3.81    full 
  2 .pred_mid_class_res_rf_1_06  rand_forest 0.674   mid  
  3 .pred_full_class_res_rf_1_07 rand_forest 0.396   full 
  4 .pred_full_class_res_rf_1_01 rand_forest 0.0544  full 
  5 .pred_full_class_res_rf_1_06 rand_forest 0.0155  full 
  6 .pred_full_class_res_rf_1_04 rand_forest 0.00356 full

Code
  st_log_1__
Message
  -- A stacked ensemble model -------------------------------------

  Out of 10 possible candidate members, the ensemble retained 2.
  Penalty: 0.01.
  Mixture: 1.

  The 2 highest weighted member classes are:
Output
  # A tibble: 2 x 3
    member                   type        weight
    <chr>                    <chr>        <dbl>
  1 .pred_no_log_res_rf_1_02 rand_forest   3.75
  2 .pred_no_log_res_rf_1_05 rand_forest   2.70