The autoxgb
strategy creates an XGBoost learner to fit a specific task. It performs
imputation if any variables are missing, and one-hot encoding if any factor variables
are present. By default, it uses the following hyperparameters:
nrounds = 200
eta = 2^-6
max_depth = 3
colsample_bytree = 0.6
subsample = 0.6
Available parameters for use in in strategy.pars
:
par.vals
(list
) List of hyperparameter values to override defaults.
impute.pars
(list
) List of parameters to pass to makeImputeWrapper
dummy.pars
(list
) List of parameters to pass to makeDummyFeaturesWrapper
The autoxgb_tuned
strategy creates an XGBoost learner to fit a specific
task and tunes it for improved performance.
Available parameters for use in in strategy.pars
:
impute.pars
(list
) List of parameters to pass to makeImputeWrapper
dummy.pars
(list
) List of parameters to pass to makeDummyFeaturesWrapper
tune.pars
(list
) list of parameters to pass to makeTuneWrapper
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