View source: R/190_SuperConfig.R
| setup_SuperConfigLive | R Documentation |
Build a SuperConfigTabular – same shape as setup_SuperConfig but with
in-memory tabular data instead of file paths.
setup_SuperConfigLive(
dat_training,
dat_validation = NULL,
dat_test = NULL,
outcome = NULL,
features = NULL,
weights = NULL,
positive_class = NULL,
preprocessor_config = NULL,
decomposition_config = NULL,
hyperparameters = NULL,
tuner_config = NULL,
outer_resampling_config = NULL,
execution_config = setup_FutureExecution(),
question = NULL,
outdir = NULL,
verbosity = 1L
)
dat_training |
data.frame or data.table. Training data. |
dat_validation |
data.frame, data.table, or |
dat_test |
data.frame, data.table, or |
outcome |
Optional Character: Name of the outcome column; |
features |
Optional Character vector: Names of the predictor
columns; |
weights |
Optional Character: Column name in |
positive_class |
Optional Character: For binary classification, the
outcome level to treat as positive; forwarded to train which reorders
the outcome factor via set_positive_class. |
preprocessor_config, hyperparameters, tuner_config, outer_resampling_config, execution_config, question, verbosity |
See setup_SuperConfig. |
decomposition_config |
Optional |
outdir |
Optional Character: Output directory; |
SuperConfigTabular object.
EDG
scl <- setup_SuperConfigLive(
dat_training = iris,
hyperparameters = setup_LightGBM(),
outer_resampling_config = setup_KFold(),
question = "Can we tell iris species apart given their measurements?"
)
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