Usage
ml_random_forest(x, response, features, max.bins = 32L, max.depth = 5L, num.trees = 20L, type = c("auto", "regression", "classification"), ...)
Arguments
x
An object coercable to a Spark DataFrame (typically, a
tbl_spark).
response
The name of the response vector (as a length-one character
vector), or a formula, giving a symbolic description of the model to be
fitted. When response is a formula, it is used in preference to other
parameters to set the response, features, and intercept
parameters (if available). Currently, only simple linear combinations of
existing parameters is supposed; e.g. response ~ feature1 + feature2 + ....
The intercept term can be omitted by using - 1 in the model fit.
features
The name of features (terms) to use for the model fit.
max.bins
The maximum number of bins used for discretizing
continuous features and for choosing how to split on features at
each node. More bins give higher granularity.
max.depth
Maximum depth of the tree (>= 0); that is, the maximum
number of nodes separating any leaves from the root of the tree.
num.trees
Number of trees to train (>= 1).
type
The type of model to fit. "regression" treats the response
as a continuous variable, while "classification" treats the response
as a categorical variable. When "auto" is used, the model type is
inferred based on the response variable type -- if it is a numeric type,
then regression is used; classification otherwise.
...
Optional arguments; currently unused.