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
The name of the response vector (as a length-one character
vector), or a formula, giving a symbolic description of the model to be
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.
Optional arguments, used to affect the model generated. See
Optional arguments. The
Other Spark ML routines: