Spark ML -- Random Forests
Perform regression or classification using random forests with a Spark DataFrame.
ml_random_forest(x, response, features, max.bins = 32L, max.depth = 5L, num.trees = 20L, 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 fitted. When
responseis a formula, it is used in preference to other parameters to set the
interceptparameters (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
- 1in 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.
Number of trees to train (>= 1).
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_optionsfor more details.
Optional arguments. The
dataargument can be used to specify the data to be used when
xis a formula; this allows calls of the form
ml_linear_regression(y ~ x, data = tbl), and is especially useful in conjunction with
Other Spark ML routines: