WPM("install-package", "XMeans")
.Feature costs are added for real prognostic models but not for baseline models.
runLlamaModels(asscenarios, feature.steps.list = NULL, baselines = NULL, learners = list(), par.sets = list(), rs.iters = 100L, n.inner.folds = 2L)
ASScenario
]
Algorithm selection scenarios.list
of character
]
Named list of feature steps we want to use.
Must be named with scenario ids.
Default is to take the default feature steps from the scenario.character
]
Vector of characters, defining the baseline models.
Default is c("vbs", "singleBest", "singleBestByPar", "singleBestBySuccesses").Learner
]
mlr learners to use for modeling.
Default is none.ParamSet
]
Param sets for learners to tune via random search.
Pass an empty param set, if you want no tuning.
Must be in of same length as learners
and in the same order.
Default is none.integer(1)
]
Number of iterations for random search hyperparameter tuning.
Default is 100.integer(1)
]
Number of cross-validation folds for inner CV in hyperparameter tuning.
Default is 2L.