AutoCompBoost: Interface function for autocompboost

In Coorsaa/autocompboost: AutoML with compboost

Interface function for autocompboost

Description

Creates an instance of AutoCompBoostClassif or AutoCompBoostRegr.

Usage

AutoCompBoost(
  task,
  resampling = NULL,
  param_values = NULL,
  measure = NULL,
  tuning_method = "smashy",
  tuning_time = 60L,
  tuning_iters = 150L,
  tuning_generations = 3L,
  enable_tuning = TRUE,
  final_model = TRUE
)

Arguments

task	(Task) Contains the task to be solved. Currently TaskClassif and TaskRegr are supported.
resampling	(Resampling) Contains the resampling method to be used for hyper-parameter optimization. Defaults to ResamplingCV with 3 folds.
param_values	(list()) Parameter values which are pass on to the learner.
measure	(Measure) Contains the performance measure, for which we optimize during training. Defaults to Accuracy for classification and RMSE for regression.
tuning_method	(character(1)) Tuning method. Possible choices are "mbo", "hyperband" or "smash"¸ Default is "mbo".
tuning_time	(integer(1)) Termination criterium. Number of seconds for which to run the optimization. Does not include training time of the final model. Default is set to 60, i.e. one minute. Tuning is terminated depending on the first termination criteria fulfilled.
tuning_iters	(integer(1)) Termination criterium. Number of MBO iterations for which to run the optimization. Default is set to 150 iterations. Tuning is terminated depending on the first termination criteria fulfilled.
tuning_generations	(integer(1)) Termination criterium for tuning method smashy. Number of generations for which to run the optimization. Default is set to 3 generations. Tuning is terminated depending on the first termination criteria fulfilled.
enable_tuning	(logical(1)) Whether or not to perform hyperparameter optimization. Default is TRUE.
final_model	(logical(1)) Whether or not to return the final model trained on the whole dataset at the end.

Value

(AutoCompBoostClassif | AutoCompBoostRegr)
Returned class depends on the type of task.

Examples

## Not run: 
library(mlr3)
library(autocompboost)

model = AutoCompBoost(tsk("sonar"))
model$train()

## End(Not run)

Coorsaa/autocompboost documentation built on March 19, 2023, 12:08 p.m.