Spark ML -- Gradient-Boosted Tree
ml_gradient_boosted_trees(x, response, features, max.bins = 32L, max.depth = 5L, type = c("auto", "regression", "classification"), ml.options = ml_options(), ...)
- An object coercable to a Spark DataFrame (typically, a
- The name of the response vector (as a length-one character
vector), or a formula, giving a symbolic description of the model to be
response is a formula, it is used in preference to other
parameters to set the
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.
- The name of features (terms) to use for the model fit.
- 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.
- Maximum depth of the tree (>= 0); that is, the maximum
number of nodes separating any leaves from the root of the tree.
- 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, used to affect the model generated. See
ml_options for more details.
- Optional arguments; currently unused.
Perform regression or classification using gradient-boosted trees.
Other Spark ML routines: