Perform regression or classification using decision trees.
ml_decision_tree(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
tbl_spark
).
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.
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. The data
argument can be used to
specify the data to be used when x
is a formula; this allows calls
of the form ml_linear_regression(y ~ x, data = tbl)
, and is
especially useful in conjunction with do
.
Other Spark ML routines: ml_als_factorization
,
ml_generalized_linear_regression
,
ml_gradient_boosted_trees
,
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