Perform regression or classification using random forests with a Spark DataFrame.
ml_random_forest(x, response, features, col.sample.rate = NULL,
impurity = c("auto", "gini", "entropy", "variance"), max.bins = 32L,
max.depth = 5L, min.info.gain = 0, min.rows = 1L, num.trees = 20L,
sample.rate = 1, thresholds = NULL, seed = NULL, type = c("auto",
"regression", "classification"), checkpoint.interval = 10L,
cache.node.ids = FALSE, max.memory = 256L, ml.options = ml_options(),
...)An object coercable to a Spark DataFrame (typically, a
tbl_spark).
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.
The name of features (terms) to use for the model fit.
The sampling rate of features to consider for splits at each tree node. Defaults to 1/3 for regression and sqrt(k)/k for classification where k is number of features. For Spark versions prior to 2.0.0, arbitrary sampling rates are not supported, so the input is automatically mapped to one of "onethird", "sqrt", or "log2".
Criterion used for information gain calculation One of 'auto', 'gini', 'entropy', or 'variance'. 'auto' defaults to 'gini' for classification and 'variance' for regression.
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.
Minimum information gain for a split to be considered at a tree node. Should be >= 0, defaults to 0.
Minimum number of instances each child must have after split.
Number of trees to train (>= 1), defaults to 20.
Fraction of the training data used for learning each decision tree, defaults to 1.0.
Thresholds in multi-class classification to adjust the probability of predicting each class. Vector must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
Seed for random numbers.
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.
Set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations, defaults to 10.
If FALSE, the algorithm will pass trees to executors to match instances with nodes.
If TRUE, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.
Defaults to FALSE.
Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. Defaults to 256.
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_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