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XGBoost Learner

Source: R/learners.R
lnr_xgboost.Rd

A wrapper for xgboost::xgb.train() for use in nadir::super_learner().

Usage

lnr_xgboost(
  data,
  formula,
  weights = NULL,
  nrounds = 1000,
  xgb.params = xgboost::xgb.params(),
  objective = NULL,
  ...
)

Arguments

data

A dataframe to train a learner / learners on.

formula

A regression formula to use inside this learner.

weights

Optional observation weights.

nrounds

Number of boosting iterations.

xgb.params

An xgboost::xgb.params() object, or a named list of parameters that can be passed to xgboost::xgb.params().

objective

Optional objective. If supplied, this is inserted into xgb.params. For binary Super Learner fits, nadir supplies objective = "binary:logistic" through outcome_type_dependent_args.

...

Additional arguments passed to xgboost::xgb.params().

Value

A prediction function that accepts newdata and returns a numeric vector of predictions.

Details

This version avoids the high-level xgboost::xgboost() wrapper and instead uses the lower-level xgb.DMatrix() + xgb.train() interface. This is more stable for programmatic Super Learner use because xgb.train() supports the full range of XGBoost objectives.

See also

learners

Examples

lnr_xgboost(mtcars, mpg ~ hp, nrounds = 5)(mtcars)
#>  [1] 20.91871 20.91871 22.07225 20.91871 19.23841 18.98325 15.27003 25.20769
#>  [9] 22.07225 19.02122 19.02122 17.14740 17.14740 17.14740 13.99621 13.99621
#> [17] 15.27003 27.78719 26.87268 27.78719 21.16343 16.97057 16.97057 15.27003
#> [25] 19.23841 27.78719 24.87027 25.82568 16.05263 19.23841 16.05263 20.91871

lnr_xgboost(
  mtcars,
  am ~ cyl + disp + hp,
  objective = "binary:logistic",
  nrounds = 5
)(mtcars)
#>  [1] 0.6155238 0.6155238 0.7153227 0.1350583 0.1350583 0.1350583 0.4025543
#>  [8] 0.5570078 0.5570078 0.1350583 0.1350583 0.1350583 0.1350583 0.1350583
#> [15] 0.1350583 0.1350583 0.4025543 0.7153227 0.7153227 0.7153227 0.6084444
#> [22] 0.1350583 0.1350583 0.4025543 0.1350583 0.7153227 0.6084444 0.7618669
#> [29] 0.4025543 0.6155238 0.4025543 0.6642613