Internal function for parsing settings related to model development
.parse_model_development_settings(
config = NULL,
data,
parallel,
outcome_type,
learner = waiver(),
hyperparameter = waiver(),
novelty_detector = waiver(),
detector_parameters = waiver(),
parallel_model_development = waiver(),
...
)List of parameters related to model development.
A list of settings, e.g. from an xml file.
Data set as loaded using the .load_data function.
Logical value that whether familiar uses parallelisation. If
FALSE it will override parallel_model_development.
Type of outcome found in the data set.
(required) One or more algorithms used for model
development. A sizeable number learners is supported in familiar. Please
see the vignette on learners for more information concerning the available
learners.
(optional) List of lists containing hyperparameters
for learners. Each sublist should have the name of the learner method it
corresponds to, with list elements being named after the intended
hyperparameter, e.g. "glm_logistic"=list("sign_size"=3)
All learners have hyperparameters. Please refer to the vignette on learners for more details. If no parameters are provided, sequential model-based optimisation is used to determine optimal hyperparameters.
Hyperparameters provided by the user are never optimised. However, if more than one value is provided for a single hyperparameter, optimisation will be conducted using these values.
(optional) Specify the algorithm used for training a novelty detector. This detector can be used to identify out-of-distribution data prospectively.
(optional) List lists containing hyperparameters for novelty detectors. Currently not used.
(optional) Enable parallel processing for
the model development workflow. Defaults to TRUE. When set to FALSE,
this will disable the use of parallel processing while developing models,
regardless of the settings of the parallel parameter.
parallel_model_development is ignored if parallel=FALSE.
Unused arguments.