Spark ML -- Gradient-Boosted Tree
Perform regression or classification using gradient-boosted trees.
ml_gradient_boosted_trees(x, response, features, impurity = c("auto", "gini", "entropy", "variance"), loss.type = c("auto", "logistic", "squared", "absolute"), max.bins = 32L, max.depth = 5L, num.trees = 20L, min.info.gain = 0, min.rows = 1L, learn.rate = 0.1, sample.rate = 1, type = c("auto", "regression", "classification"), thresholds = NULL, seed = NULL, checkpoint.interval = 10L, cache.node.ids = FALSE, max.memory = 256L, 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
The name of features (terms) to use for the model fit.
Criterion used for information gain calculation One of 'auto', 'gini', 'entropy', or 'variance'. 'auto' defaults to 'gini' for classification and 'variance' for regression.
Loss function which the algorithm tries to minimize. Defaults to
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), defaults to 20.
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.
The learning rate or step size, defaults to 0.1.
Fraction of the training data used for learning each decision tree, defaults to 1.0.
The type of model to fit.
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.
Set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations, defaults to 10.
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
Optional arguments. The
Other Spark ML routines: