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(), ...)

- 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. 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`