                                Package ‘tidyAML’
                                                April 20, 2023
Title Automatic Machine Learning with 'tidymodels'
Version 0.0.2
Description The goal of this package will be to provide a simple interface for automatic ma-
      chine learning that fits the 'tidymodels' framework. The intention is to work for regres-
      sion and classification problems with a simple verb framework.
License MIT + file LICENSE
Encoding UTF-8
RoxygenNote 7.2.3
URL https://github.com/spsanderson/tidyAML
BugReports https://github.com/spsanderson/tidyAML/issues
Depends parsnip, R (>= 3.4.0)
Suggests knitr, rmarkdown, roxygen2, stats, tibble, stringr, utils,
      recipes
VignetteBuilder knitr
Imports magrittr, rlang (>= 0.4.11), purrr (>= 0.3.5), dplyr (>=
      1.0.10), rsample (>= 1.1.0), workflows (>= 1.1.2), forcats,
      workflowsets
NeedsCompilation no
Author Steven Sanderson [aut, cre, cph]
Maintainer Steven Sanderson <spsanderson@gmail.com>
Repository CRAN
Date/Publication 2023-04-20 13:40:02 UTC
R topics documented:
         core_packages . . . .    .   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
         create_model_spec .      .   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
         create_splits . . . . .  .   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
         create_workflow_set      .   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
         extract_model_spec .     .   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
                                                        1
2                                                                                            core_packages
         extract_wflw . . . . . . . . . . . . .  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  7
         extract_wflw_fit . . . . . . . . . . .  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  8
         extract_wflw_pred . . . . . . . . .     . . . . . . . . . . . . . . . . . . . . . . . . . . . .  9
         fast_classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
         fast_classification_parsnip_spec_tbl    . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
         fast_regression . . . . . . . . . . .   . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
         fast_regression_parsnip_spec_tbl . .    . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
         get_model . . . . . . . . . . . . . .   . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
         install_deps . . . . . . . . . . . . .  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
         internal_make_fitted_wflw . . . . .     . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
         internal_make_spec_tbl . . . . . . .    . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
         internal_make_wflw . . . . . . . .      . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
         internal_make_wflw_predictions . .      . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
         internal_set_args_to_tune . . . . . .   . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
         load_deps . . . . . . . . . . . . . .   . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
         make_classification_base_tbl . . . .    . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
         make_regression_base_tbl . . . . .      . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
         match_args . . . . . . . . . . . . .    . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Index                                                                                                    26
  core_packages                Functions to Install all Core Libraries
Description
    Lists the core packages necessary to run all potential modeling algorithms.
Usage
    core_packages()
Details
    Lists the core packages necessary to run all potential modeling algorithms.
Value
    A character vector
Author(s)
    Steven P. Sanderson II, MPH
See Also
    Other Utility: create_splits(), create_workflow_set(), fast_classification_parsnip_spec_tbl(),
    fast_regression_parsnip_spec_tbl(), install_deps(), load_deps(), match_args()
create_model_spec                                                                                            3
Examples
     core_packages()
   create_model_spec              Generate Model Specification calls to parsnip
Description
     Creates a list/tibble of parsnip model specifications.
Usage
     create_model_spec(
       .parsnip_eng = list("lm"),
       .mode = list("regression"),
       .parsnip_fns = list("linear_reg"),
       .return_tibble = TRUE
     )
Arguments
     .parsnip_eng         The input must be a list. The default for this is set to all. This means that all
                          of the parsnip linear regression engines will be used, for example lm, or glm.
                          You can also choose to pass a c() vector like c('lm', 'glm')
     .mode                The input must be a list. The default is ’regression’
     .parsnip_fns         The input must be a list. The default for this is set to all. This means that all of
                          the parsnip linear regression functions will be used, for example linear_reg(),
                          or cubist_rules. You can also choose to pass a c() vector like c("linear_reg","cubist_rules")
     .return_tibble The default is TRUE. FALSE will return a list object.
Details
     Creates a list/tibble of parsnip model specifications. With this function you can generate a list/tibble
     output of any model specification and engine you choose that is supported by the parsnip ecosys-
     tem.
Value
     A list or a tibble.
Author(s)
     Steven P. Sanderson II, MPH
4                                                                                                  create_splits
See Also
    Other Model_Generator: fast_classification(), fast_regression()
Examples
    create_model_spec(
     .parsnip_eng = list("lm","glm","glmnet","cubist"),
     .parsnip_fns = list(
            rep(
              "linear_reg", 3),
              "cubist_rules"
          )
     )
    create_model_spec(
     .parsnip_eng = list("lm","glm","glmnet","cubist"),
     .parsnip_fns = list(
            rep(
              "linear_reg", 3),
              "cubist_rules"
          ),
     .return_tibble = FALSE
     )
  create_splits                  Utility Create Splits Object
Description
    Create a splits object.
Usage
    create_splits(.data, .split_type = "initial_split", .split_args = NULL)
Arguments
    .data               The data being passed to make a split on
    .split_type         The default is "initial_split", you can pass any other split type from the rsample
                        library.
    .split_args         The default is NULL in order to use the default split arguments. If you want
                        to pass other arguments then must pass a list with the parameter name and the
                        argument.
Details
    Create a splits object that returns a list object of both the splits object itself and the splits type. This
    function supports all splits types from the rsample package.
create_workflow_set                                                                                     5
Value
     A list object
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Utility: core_packages(), create_workflow_set(), fast_classification_parsnip_spec_tbl(),
     fast_regression_parsnip_spec_tbl(), install_deps(), load_deps(), match_args()
Examples
     create_splits(mtcars, .split_type = "vfold_cv")
   create_workflow_set          Create a Workflow Set Object
Description
     Create a workflow set object tibble from a model spec tibble.
Usage
     create_workflow_set(.model_tbl = NULL, .recipe_list = list(), .cross = TRUE)
Arguments
     .model_tbl          The model table that is generated from a function like fast_regression_parsnip_spec_tbl().
                         The model spec column will be grabbed automatically as the class of the object
                         must be tidyaml_base_tbl
     .recipe_list        Provide a list of recipes here that will get added to the workflow set object.
     .cross              The default is TRUE, can be set to FALSE. This is passed to the cross parameter
                         as an argument to the workflow_set() function.
Details
     Create a workflow set object/tibble from a model spec tibble where the object class type is
     tidyaml_base_tbl. This function will take in a list of recipes and will grab the model specifi-
     cations from the base tibble to create the workflow sets object. You can also supply the logical
     of TRUE/FALSe the .cross parameter which gets passed to the corresponding parameter as an
     argumnt to the workflowsets::workflow_set() function.
Value
     A list object of workflows.
6                                                                                    extract_model_spec
Author(s)
    Steven P. Sanderson II, MPH
See Also
    https://workflowsets.tidymodels.org/
    Other Utility: core_packages(), create_splits(), fast_classification_parsnip_spec_tbl(),
    fast_regression_parsnip_spec_tbl(), install_deps(), load_deps(), match_args()
Examples
    library(recipes)
    rec_obj <- recipe(mpg ~ ., data = mtcars)
    spec_tbl <- fast_regression_parsnip_spec_tbl(
       .parsnip_fns = "linear_reg",
       .parsnip_eng = c("lm","glm")
    )
    create_workflow_set(
       spec_tbl,
       list(rec_obj)
    )
  extract_model_spec            Extract A Model Specification
Description
    Extract a model specification from a tidyAML model tibble.
Usage
    extract_model_spec(.data, .model_id = NULL)
Arguments
    .data               The model table that must have the class tidyaml_mod_spec_tbl.
    .model_id           The model number that you want to select, Must be an integer or sequence of
                        integers, ie. 1 or c(1,3,5) or 1:2
Details
    This function allows you to get a model specification or more from a tibble with a class of "tidyaml_mod_spec_tbl".
    It allows you to select the model by the .model_id column. You can call the model id’s by an inte-
    ger or a sequence of integers.
extract_wflw                                                                                           7
Value
     A tibble with the chosen model specification(s).
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Extractor: extract_wflw_fit(), extract_wflw_pred(), extract_wflw(), get_model()
Examples
     library(recipes)
     rec_obj <- recipe(mpg ~ ., data = mtcars)
     spec_tbl <- fast_regression_parsnip_spec_tbl(
        .parsnip_fns = "linear_reg",
        .parsnip_eng = c("lm","glm")
     )
     extract_model_spec(spec_tbl, 1)
     extract_model_spec(spec_tbl, 1:2)
  extract_wflw                   Extract A Model Workflow
Description
     Extract a model workflow from a tidyAML model tibble.
Usage
     extract_wflw(.data, .model_id = NULL)
Arguments
     .data               The model table that must have the class tidyaml_mod_spec_tbl.
     .model_id           The model number that you want to select, Must be an integer or sequence of
                         integers, ie. 1 or c(1,3,5) or 1:2
Details
     This function allows you to get a model workflow or more from a tibble with a class of "tidyaml_mod_spec_tbl".
     It allows you to select the model by the .model_id column. You can call the model id’s by an inte-
     ger or a sequence of integers.
8                                                                                  extract_wflw_fit
Value
    A tibble with the chosen model workflow(s).
Author(s)
    Steven P. Sanderson II, MPH
See Also
    Other Extractor: extract_model_spec(), extract_wflw_fit(), extract_wflw_pred(), get_model()
Examples
    library(recipes)
    rec_obj <- recipe(mpg ~ ., data = mtcars)
    frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"),
                                                     .parsnip_fns = "linear_reg")
    extract_wflw(frt_tbl, 1)
    extract_wflw(frt_tbl, 1:2)
  extract_wflw_fit             Extract A Model Fitted Workflow
Description
    Extract a model fitted workflow from a tidyAML model tibble.
Usage
    extract_wflw_fit(.data, .model_id = NULL)
Arguments
    .data               The model table that must have the class tidyaml_mod_spec_tbl.
    .model_id           The model number that you want to select, Must be an integer or sequence of
                        integers, ie. 1 or c(1,3,5) or 1:2
Details
    This function allows you to get a model fitted workflow or more from a tibble with a class of
    "tidyaml_mod_spec_tbl". It allows you to select the model by the .model_id column. You can call
    the model id’s by an integer or a sequence of integers.
Value
    A tibble with the chosen model workflow(s).
extract_wflw_pred                                                                                     9
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Extractor: extract_model_spec(), extract_wflw_pred(), extract_wflw(), get_model()
Examples
     library(recipes)
     rec_obj <- recipe(mpg ~ ., data = mtcars)
     frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"),
                                                      .parsnip_fns = "linear_reg")
     extract_wflw_fit(frt_tbl, 1)
     extract_wflw_fit(frt_tbl, 1:2)
  extract_wflw_pred             Extract A Model Workflow Predictions
Description
     Extract a model workflow predictions from a tidyAML model tibble.
Usage
     extract_wflw_pred(.data, .model_id = NULL)
Arguments
     .data               The model table that must have the class tidyaml_mod_spec_tbl.
     .model_id           The model number that you want to select, Must be an integer or sequence of
                         integers, ie. 1 or c(1,3,5) or 1:2
Details
     This function allows you to get a model workflow predictions or more from a tibble with a class of
     "tidyaml_mod_spec_tbl". It allows you to select the model by the .model_id column. You can call
     the model id’s by an integer or a sequence of integers.
Value
     A tibble with the chosen model workflow(s).
Author(s)
     Steven P. Sanderson II, MPH
10                                                                                           fast_classification
See Also
     Other Extractor: extract_model_spec(), extract_wflw_fit(), extract_wflw(), get_model()
Examples
     library(recipes)
     rec_obj <- recipe(mpg ~ ., data = mtcars)
     frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"),
                                                           .parsnip_fns = "linear_reg")
     extract_wflw_pred(frt_tbl, 1)
     extract_wflw_pred(frt_tbl, 1:2)
   fast_classification            Generate Model Specification calls to parsnip
Description
     Creates a list/tibble of parsnip model specifications.
Usage
     fast_classification(
       .data,
       .rec_obj,
       .parsnip_fns = "all",
       .parsnip_eng = "all",
       .split_type = "initial_split",
       .split_args = NULL
     )
Arguments
     .data                The data being passed to the function for the classification problem
     .rec_obj             The recipe object being passed.
     .parsnip_fns         The default is ’all’ which will create all possible classification model specifica-
                          tions supported.
     .parsnip_eng         the default is ’all’ which will create all possible classification model specifica-
                          tions supported.
     .split_type          The default is ’initial_split’, you can pass any type of split supported by rsample
     .split_args          The default is NULL, when NULL then the default parameters of the split type
                          will be executed for the rsample split type.
fast_classification_parsnip_spec_tbl                                                                11
Details
     With this function you can generate a tibble output of any classification model specification and
     it’s fitted workflow object. Per recipes documentation explicitly with step_string2factor() it is
     encouraged to mutate your predictor into a factor before you create your recipe.
Value
     A list or a tibble.
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Model_Generator: create_model_spec(), fast_regression()
Examples
     library(recipes, quietly = TRUE)
     library(dplyr, quietly = TRUE)
     df <- mtcars %>% mutate(cyl = as.factor(cyl))
     rec_obj <- recipe(cyl ~ ., data = df)
     fct_tbl <- fast_classification(
        .data = df,
        .rec_obj = rec_obj,
        .parsnip_eng = c("glm","LiblineaR"))
     glimpse(fct_tbl)
   fast_classification_parsnip_spec_tbl
                                 Utility Classification call to parsnip
Description
     Creates a tibble of parsnip classification model specifications.
Usage
     fast_classification_parsnip_spec_tbl(
        .parsnip_fns = "all",
        .parsnip_eng = "all"
     )
12                                                                                             fast_regression
Arguments
     .parsnip_fns         The default for this is set to all. This means that all of the parsnip classifica-
                          tion functions will be used, for example bag_mars(), or bart(). You can also
                          choose to pass a c() vector like c("barg_mars","bart")
     .parsnip_eng         The default for this is set to all. This means that all of the parsnip classification
                          engines will be used, for example earth, or dbarts. You can also choose to
                          pass a c() vector like c('earth', 'dbarts')
Details
     Creates a tibble of parsnip classification model specifications. This will create a tibble of 32 dif-
     ferent classification model specifications which can be filtered. The model specs are created first
     and then filtered out. This will only create models for classification problems. To find all of the
     supported models in this package you can visit https://www.tidymodels.org/find/parsnip/
Value
     A tibble with an added class of ’fst_class_spec_tbl’
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_regression_parsnip_spec_tbl()
     install_deps(), load_deps(), match_args()
Examples
     fast_classification_parsnip_spec_tbl(.parsnip_fns = "logistic_reg")
     fast_classification_parsnip_spec_tbl(.parsnip_eng = c("earth","dbarts"))
   fast_regression                Generate Model Specification calls to parsnip
Description
     Creates a list/tibble of parsnip model specifications.
fast_regression                                                                                           13
Usage
     fast_regression(
         .data,
         .rec_obj,
         .parsnip_fns = "all",
         .parsnip_eng = "all",
         .split_type = "initial_split",
         .split_args = NULL
     )
Arguments
     .data               The data being passed to the function for the regression problem
     .rec_obj            The recipe object being passed.
     .parsnip_fns        The default is ’all’ which will create all possible regression model specifications
                         supported.
     .parsnip_eng        the default is ’all’ which will create all possible regression model specifications
                         supported.
     .split_type         The default is ’initial_split’, you can pass any type of split supported by rsample
     .split_args         The default is NULL, when NULL then the default parameters of the split type
                         will be executed for the rsample split type.
Details
     With this function you can generate a tibble output of any regression model specification and it’s
     fitted workflow object.
Value
     A list or a tibble.
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Model_Generator: create_model_spec(), fast_classification()
Examples
     library(recipes, quietly = TRUE)
     library(dplyr, quietly = TRUE)
     rec_obj <- recipe(mpg ~ ., data = mtcars)
     frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"),
     .parsnip_fns = "linear_reg")
     glimpse(frt_tbl)
14                                                                        fast_regression_parsnip_spec_tbl
   fast_regression_parsnip_spec_tbl
                                Utility Regression call to parsnip
Description
     Creates a tibble of parsnip regression model specifications.
Usage
     fast_regression_parsnip_spec_tbl(.parsnip_fns = "all", .parsnip_eng = "all")
Arguments
     .parsnip_fns        The default for this is set to all. This means that all of the parsnip linear regres-
                         sion functions will be used, for example linear_reg(), or cubist_rules. You
                         can also choose to pass a c() vector like c("linear_reg","cubist_rules")
     .parsnip_eng        The default for this is set to all. This means that all of the parsnip linear
                         regression engines will be used, for example lm, or glm. You can also choose
                         to pass a c() vector like c('lm', 'glm')
Details
     Creates a tibble of parsnip regression model specifications. This will create a tibble of 46 different
     regression model specifications which can be filtered. The model specs are created first and then
     filtered out. This will only create models for regression problems. To find all of the supported
     models in this package you can visit https://www.tidymodels.org/find/parsnip/
Value
     A tibble with an added class of ’fst_reg_spec_tbl’
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_classification_parsnip_spec_t
     install_deps(), load_deps(), match_args()
Examples
     fast_regression_parsnip_spec_tbl(.parsnip_fns = "linear_reg")
     fast_regression_parsnip_spec_tbl(.parsnip_eng = c("lm","glm"))
get_model                                                                                           15
  get_model                     Get a Model
Description
    Get a model from a tidyAML model tibble.
Usage
    get_model(.data, .model_id = NULL)
Arguments
    .data               The model table that must have the class tidyaml_mod_spec_tbl.
    .model_id           The model number that you want to select, Must be an integer or sequence of
                        integers, ie. 1 or c(1,3,5) or 1:2
Details
    This function allows you to get a model or models from a tibble with a class of "tidyaml_mod_spec_tbl".
    It allows you to select the model by the .model_id column. You can call the model id’s by an inte-
    ger or a sequence of integers.
Value
    A tibble with the chosen models.
Author(s)
    Steven P. Sanderson II, MPH
See Also
    Other Extractor: extract_model_spec(), extract_wflw_fit(), extract_wflw_pred(), extract_wflw()
Examples
    library(recipes)
    rec_obj <- recipe(mpg ~ ., data = mtcars)
    spec_tbl <- fast_regression_parsnip_spec_tbl(
       .parsnip_fns = "linear_reg",
       .parsnip_eng = c("lm","glm")
    )
    get_model(spec_tbl, 1)
    get_model(spec_tbl, 1:2)
16                                                                           internal_make_fitted_wflw
   install_deps                  Functions to Install all Core Libraries
Description
     Installs all dependencies in the core_packages() function.
Usage
     install_deps()
Details
     Installs all dependencies in the core_packages() function.
Value
     No return value, called for side effects
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_classification_parsnip_spec_t
     fast_regression_parsnip_spec_tbl(), load_deps(), match_args()
Examples
     ## Not run:
       install_deps()
     ## End(Not run)
   internal_make_fitted_wflw
                                 Internals Safely Make a Fitted Workflow from Model Spec tibble
Description
     Safely Make a fitted workflow from a model spec tibble.
Usage
     internal_make_fitted_wflw(.model_tbl, .splits_obj)
internal_make_fitted_wflw                                                                               17
Arguments
     .model_tbl          The model table that is generated from a function like fast_regression_parsnip_spec_tbl(),
                         must have a class of "tidyaml_mod_spec_tbl". This is meant to be used after the
                         function internal_make_wflw() has been run and the tibble has been saved.
     .splits_obj         The splits object from the auto_ml function. It is internal to the auto_ml_ func-
                         tion.
Details
     Create a fitted parnsip model from a workflow object.
Value
     A list object of workflows.
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Internals: internal_make_spec_tbl(), internal_make_wflw_predictions(), internal_make_wflw(),
     internal_set_args_to_tune(), make_classification_base_tbl(), make_regression_base_tbl()
Examples
     library(recipes, quietly = TRUE)
     library(dplyr, quietly = TRUE)
     mod_spec_tbl <- fast_regression_parsnip_spec_tbl(
       .parsnip_eng = c("lm","glm","gee"),
       .parsnip_fns = "linear_reg"
     )
     rec_obj <- recipe(mpg ~ ., data = mtcars)
     splits_obj <- create_splits(mtcars, "initial_split")
     mod_tbl <- mod_spec_tbl %>%
       mutate(wflw = internal_make_wflw(mod_spec_tbl, rec_obj))
     internal_make_fitted_wflw(mod_tbl, splits_obj)
18                                                                                internal_make_spec_tbl
   internal_make_spec_tbl
                                Internals Make a Model Spec tibble
Description
     Make a Model Spec tibble.
Usage
     internal_make_spec_tbl(.data)
Arguments
     .data              This is the data that should be coming from inside of the regression/classification
                        to parsnip spec functions.
Details
     Make a Model Spec tibble.
Value
     A model spec tbl.
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Internals: internal_make_fitted_wflw(), internal_make_wflw_predictions(), internal_make_wflw(),
     internal_set_args_to_tune(), make_classification_base_tbl(), make_regression_base_tbl()
Examples
     make_regression_base_tbl() %>%
       internal_make_spec_tbl()
     make_classification_base_tbl() %>%
       internal_make_spec_tbl()
internal_make_wflw                                                                                 19
   internal_make_wflw           Internals Safely Make Workflow from Model Spec tibble
Description
     Safely Make a workflow from a model spec tibble.
Usage
     internal_make_wflw(.model_tbl, .rec_obj)
Arguments
     .model_tbl          The model table that is generated from a function like fast_regression_parsnip_spec_tbl(),
                         must have a class of "tidyaml_mod_spec_tbl".
     .rec_obj            The recipe object that is going to be used to make the workflow object.
Details
     Create a model specification tibble that has a workflows::workflow() list column.
Value
     A list object of workflows.
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw_predictions(),
     internal_set_args_to_tune(), make_classification_base_tbl(), make_regression_base_tbl()
Examples
     library(recipes, quietly = TRUE)
     library(dplyr, quietly = TRUE)
     mod_spec_tbl <- fast_regression_parsnip_spec_tbl(
       .parsnip_eng = c("lm","glm","gee"),
       .parsnip_fns = "linear_reg"
     )
     rec_obj <- recipe(mpg ~ ., data = mtcars)
     internal_make_wflw(mod_spec_tbl, rec_obj)
20                                                                       internal_make_wflw_predictions
   internal_make_wflw_predictions
                                 Internals Safely Make Predictions on a Fitted Workflow from Model
                                 Spec tibble
Description
     Safely Make predictions on a fitted workflow from a model spec tibble.
Usage
     internal_make_wflw_predictions(.model_tbl, .splits_obj)
Arguments
     .model_tbl          The model table that is generated from a function like fast_regression_parsnip_spec_tbl(),
                         must have a class of "tidyaml_mod_spec_tbl". This is meant to be used after the
                         function internal_make_fitted_wflw() has been run and the tibble has been
                         saved.
     .splits_obj         The splits object from the auto_ml function. It is internal to the auto_ml_ func-
                         tion.
Details
     Create predictions on a fitted parnsip model from a workflow object.
Value
     A list object of workflows.
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw(),
     internal_set_args_to_tune(), make_classification_base_tbl(), make_regression_base_tbl()
Examples
     library(recipes, quietly = TRUE)
     library(dplyr, quietly = TRUE)
     mod_spec_tbl <- fast_regression_parsnip_spec_tbl(
       .parsnip_eng = c("lm","glm","gee"),
       .parsnip_fns = "linear_reg"
     )
internal_set_args_to_tune                                                                            21
     rec_obj <- recipe(mpg ~ ., data = mtcars)
     splits_obj <- create_splits(mtcars, "initial_split")
     mod_tbl <- mod_spec_tbl %>%
       mutate(wflw = internal_make_wflw(mod_spec_tbl, rec_obj))
     mod_fitted_tbl <- mod_tbl %>%
       mutate(fitted_wflw = internal_make_fitted_wflw(mod_tbl, splits_obj))
     internal_make_wflw_predictions(mod_fitted_tbl, splits_obj)
   internal_set_args_to_tune
                                Internals Make a Tunable Model Specification
Description
     Make a tuned model specification object.
Usage
     internal_set_args_to_tune(.model_tbl)
Arguments
     .model_tbl          The model table that is generated from a function like fast_regression_parsnip_spec_tbl(),
                         must have a class of "tidyaml_mod_spec_tbl".
Details
     This will take a model specification that is created from a function like fast_regression_parsnip_spec_tbl()
     and update the model_spec args to tune::tune(). This is done dynamically, meaning you do not
     need to know the names of the parameters inside of the model specification.
Value
     A list object of workflows.
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw_predictions(),
     internal_make_wflw(), make_classification_base_tbl(), make_regression_base_tbl()
22                                                                                  load_deps
Examples
     library(dplyr)
     mod_tbl <- fast_regression_parsnip_spec_tbl()
     mod_tbl$model_spec[[1]]
     updated_mod_tbl <- mod_tbl %>%
       mutate(model_spec = internal_set_args_to_tune(mod_tbl))
     updated_mod_tbl$model_spec[[1]]
   load_deps                    Functions to Install all Core Libraries
Description
     Load all the core packages necessary to run all potential modeling algorithms.
Usage
     load_deps()
Details
     Load all the core packages necessary to run all potential modeling algorithms.
Value
     No return value, called for side effects
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_classification_parsnip_spec_t
     fast_regression_parsnip_spec_tbl(), install_deps(), match_args()
Examples
     ## Not run:
     load_deps()
     ## End(Not run)
make_classification_base_tbl                                                           23
  make_classification_base_tbl
                                Internals Make Base Classification Tibble
Description
    Creates a base tibble to create parsnip classification model specifications.
Usage
    make_classification_base_tbl()
Details
    Creates a base tibble to create parsnip classification model specifications.
Value
    A tibble
Author(s)
    Steven P. Sanderson II, MPH
See Also
    Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw_predictions(),
    internal_make_wflw(), internal_set_args_to_tune(), make_regression_base_tbl()
Examples
    make_classification_base_tbl()
  make_regression_base_tbl
                                Internals Make Base Regression Tibble
Description
    Creates a base tibble to create parsnip regression model specifications.
Usage
    make_regression_base_tbl()
24                                                                                        match_args
Details
     Creates a base tibble to create parsnip regression model specifications.
Value
     A tibble
Author(s)
     Steven P. Sanderson II, MPH
See Also
     Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw_predictions(),
     internal_make_wflw(), internal_set_args_to_tune(), make_classification_base_tbl()
Examples
     make_regression_base_tbl()
   match_args                    Match function arguments
Description
     Match a functions arguments.
Usage
     match_args(f, args)
Arguments
     f                   The parsnip function such as "linear_reg" as a string and without the paren-
                         theses.
     args                The arguments you want to supply to f
Details
     Match a functions arguments, the bad ones passed will be rejected but the remaining passing ones
     will be returned.
Value
     A list of matched arguments.
match_args                                                                              25
Author(s)
    Steven P. Sanderson II, MPH
See Also
    Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_classification_parsnip_spec_t
    fast_regression_parsnip_spec_tbl(), install_deps(), load_deps()
Examples
    library(parsnip)
    match_args(
      f = "linear_reg",
      args = list(
         mode = "regression",
         engine = "lm",
         trees = 1,
         mtry = 1
       )
     )
Index
∗ Extractor                                      fast_classification, 4, 10, 13
     extract_model_spec, 6                       fast_classification_parsnip_spec_tbl,
     extract_wflw, 7                                     2, 5, 6, 11, 14, 16, 22, 25
     extract_wflw_fit, 8                         fast_regression, 4, 11, 12
     extract_wflw_pred, 9                        fast_regression_parsnip_spec_tbl, 2, 5,
     get_model, 15                                       6, 12, 14, 16, 22, 25
∗ Internals                                      fast_regression_parsnip_spec_tbl(), 21
     internal_make_fitted_wflw, 16
     internal_make_spec_tbl, 18                  get_model, 7–10, 15
     internal_make_wflw, 19
     internal_make_wflw_predictions, 20          install_deps, 2, 5, 6, 12, 14, 16, 22, 25
     internal_set_args_to_tune, 21               internal_make_fitted_wflw, 16, 18–21, 23,
     make_classification_base_tbl, 23                    24
     make_regression_base_tbl, 23                internal_make_spec_tbl, 17, 18, 19–21, 23,
∗ Model_Generator                                        24
     create_model_spec, 3                        internal_make_wflw, 17, 18, 19, 20, 21, 23,
     fast_classification, 10                             24
     fast_regression, 12                         internal_make_wflw_predictions, 17–19,
∗ Utility                                                20, 21, 23, 24
     core_packages, 2                            internal_set_args_to_tune, 17–20, 21, 23,
     create_splits, 4                                    24
     create_workflow_set, 5
                                                 load_deps, 2, 5, 6, 12, 14, 16, 22, 25
     fast_classification_parsnip_spec_tbl,
          11                                     make_classification_base_tbl, 17–21, 23,
     fast_regression_parsnip_spec_tbl,                   24
          14                                     make_regression_base_tbl, 17–21, 23, 23
     install_deps, 16                            match_args, 2, 5, 6, 12, 14, 16, 22, 24
     load_deps, 22
     match_args, 24                              workflows::workflow(), 19
                                                 workflowsets::workflow_set(), 5
core_packages, 2, 5, 6, 12, 14, 16, 22, 25
create_model_spec, 3, 11, 13
create_splits, 2, 4, 6, 12, 14, 16, 22, 25
create_workflow_set, 2, 5, 5, 12, 14, 16, 22,
          25
extract_model_spec, 6, 8–10, 15
extract_wflw, 7, 7, 9, 10, 15
extract_wflw_fit, 7, 8, 8, 10, 15
extract_wflw_pred, 7–9, 9, 15
                                              26
