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Gradient Boosting Machines Learner

Source: R/learners.R
lnr_gbm.Rd

A wrapper for gbm::gbm() for use in nadir::super_learner().

Usage

lnr_gbm(
  data,
  formula,
  weights = NULL,
  verbose = FALSE,
  n.minobsinnode = 0,
  ...
)

Arguments

data

A dataframe to train a learner / learners on.

formula

A regression formula to use inside this learner.

weights

Observation weights passed to gbm::gbm(). Defaults to NULL, in which case equal weights (rep(1, nrow(data))) are used.

verbose

(default: FALSE) if set to TRUE, information about the automatic outcome type inferred by gbm will be messaged to the console, as well as the number of trees used.

n.minobsinnode

(default: 0) An integer specifying the minimum number of observations in the terminal nodes of the trees. See the gbm documentation for more. Set here to 0 to account for the potential of very small splits in cross-fitting.

...

Any extra arguments that should be passed to the internal model for model fitting purposes.

Value

A prediction function that accepts newdata, which returns predictions (a numeric vector of values, one for each row of newdata).

See also

learners

Examples

lnr_gbm(mtcars, mpg ~ hp)(mtcars)
#>  [1] 21.65804 21.65804 23.04591 21.65804 17.64625 21.33453 13.81187 27.00216
#>  [9] 22.80081 19.34433 19.34433 16.07177 16.07177 16.07177 12.90349 12.90349
#> [17] 13.81187 29.98783 27.85215 32.91892 22.12126 17.34718 17.34718 13.81187
#> [25] 17.64625 29.98783 24.87716 22.60625 14.02533 17.64625 14.02533 21.65804