R: Spark ML - Random Forests

ml_random_forest {sparklyr} | R Documentation

## Spark ML – Random Forests

### Description

Perform regression or classification using random forests with a Spark DataFrame.

### Usage

    ml_random_forest(x, response, features, max.bins = 32L, max.depth = 5L,
      num.trees = 20L, type = c("auto", "regression", "classification"),
      ml.options = ml_options(), ...)

### Arguments

`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.
`num.trees` | Number of trees to train (>= 1).
`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`.

### See Also

Other Spark ML routines: `ml_als_factorization`, `ml_decision_tree`, `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_survival_regression`

[Package sparklyr version 0.5.4 Index]