h2o_rf_train: Wrapper for training a h2o.randomForest model as part of a...

In stevenpawley/h2oparsnip: Model Wrappers for H2O models

Wrapper for training a h2o.randomForest model as part of a parsnip 'rand_forest' h2o engine

Description

Wrapper for training a h2o.randomForest model as part of a parsnip 'rand_forest' h2o engine

Usage

h2o_rf_train(
  formula,
  data,
  ntrees = 50,
  min_rows = 10,
  mtries = -1,
  stopping_rounds = 0,
  validation = 0,
  ...
)

Arguments

formula	formula
data	data.frame of training data
ntrees	integer, the number of trees to build (default = 50)
min_rows	integer, the minimum number of observations for a leaf (default = 10)
mtries	integer, the number of columns to randomly select at each level. Default of -1 is sqrt(p) for classification and (p/3) for regression.
stopping_rounds	An integer specifying the number of training iterations without improvement before stopping. If 'stopping_rounds = 0' (the default) then early stopping is disabled. If 'validation' is used, performance is base on the validation set; otherwise the training set is used.
validation	A positive number. If on '[0, 1)' the value, 'validation' is a random proportion of data in 'x' and 'y' that are used for performance assessment and potential early stopping. If 1 or greater, it is the _number_ of training set samples use for these purposes.
...	other arguments not currently used

Value

evaluated h2o model call

stevenpawley/h2oparsnip documentation built on June 20, 2022, 12:48 p.m.