# Spark ML -- Gradient-Boosted Tree

## Usage

ml_gradient_boosted_trees(x, response, features, max.bins = 32L, max.depth = 5L, type = c("auto", "regression", "classification"), ml.options = ml_options(), ...)

## 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.
- 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.
- ml.options
- Optional arguments, used to affect the model generated. See
`ml_options`

for more details.
- ...
- Optional arguments; currently unused.

## Description

Perform regression or classification using gradient-boosted trees.

## See also

Other Spark ML routines:

`ml_als_factorization`

,

`ml_decision_tree`

,

`ml_generalized_linear_regression`

,

`ml_kmeans`

,

`ml_lda`

,

`ml_linear_regression`

,

`ml_logistic_regression`

,

`ml_multilayer_perceptron`

,

`ml_naive_bayes`

,

`ml_one_vs_rest`

,

`ml_pca`

,

`ml_random_forest`

,

`ml_survival_regression`