.parse_model_development_settings | R Documentation |
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(),
...
)
config |
A list of settings, e.g. from an xml file. |
data |
Data set as loaded using the |
parallel |
Logical value that whether familiar uses parallelisation. If
|
outcome_type |
Type of outcome found in the data set. |
learner |
(required) One or more algorithms used for model
development. A sizeable number learners is supported in |
hyperparameter |
(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. 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. |
novelty_detector |
(optional) Specify the algorithm used for training a novelty detector. This detector can be used to identify out-of-distribution data prospectively. |
detector_parameters |
(optional) List lists containing hyperparameters for novelty detectors. Currently not used. |
parallel_model_development |
(optional) Enable parallel processing for
the model development workflow. Defaults to |
... |
Unused arguments. |
List of parameters related to model development.
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