dynamic_resample: Dynamic resampling

In messamat/globalIRmap: What the Package Does (One Line, Title Case)

Description

Runs a train-predict-test routine a.k.a. a resampling (possibly in parallel): Repeatedly apply learner on a training set of task to train a model, then use the trained model to predict observations of a test set. Training and test sets are defined by the resampling.

Usage

dynamic_resample(
  in_task,
  in_learner,
  in_resampling,
  type,
  store_models = FALSE
)

Arguments

in_task	Task to resample
in_learner	Learner to use in resampling (e.g., conditionel inference forest with minority class oversampling). Can be a simple learner or an Autotuner already including hyperparameter tuning strategy.
in_resampling	Resampling cross-validation strategy
type	(character) Type of learner 'classif' or 'regr'. Other values are not accepted.
store_models	whether to keep the fitted model after the test set has been predicted. Set to TRUE if you want to further analyse the models or want to extract information like variable importance.

Details

The dynamic aspect of this model is that it runs 'reset_tuning' on the fly to make sure that the hyperparameter search space matches the task (e.g., if the number of candidate predictor variables has been reduced, it adjusts mtry)

Value

ResampleResult

messamat/globalIRmap documentation built on July 4, 2021, 10:48 a.m.