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
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; currently unused.
Other Spark ML routines: