ml_gradient_boosted_trees {sparklyr} | R Documentation |
Perform regression or classification using gradient-boosted trees.
ml_gradient_boosted_trees(x, response, features, max.bins = 32L, max.depth = 5L, type = c("auto", "regression", "classification"), ml.options = ml_options(), ...)
x |
An object coercable to a Spark DataFrame (typically, a
|
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 |
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. |
ml.options |
Optional arguments, used to affect the model generated. See
|
... |
Optional arguments. The |
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