0th

Percentile

##### Spark ML -- Gradient-Boosted Tree

Perform regression or classification using gradient-boosted trees.

##### Usage
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(), ...)
##### 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.

impurity

Criterion used for information gain calculation One of 'auto', 'gini', 'entropy', or 'variance'. 'auto' defaults to 'gini' for classification and 'variance' for regression.

loss.type

Loss function which the algorithm tries to minimize. Defaults to logistic for classification and squared for regression.

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), defaults to 20.

min.info.gain

Minimum information gain for a split to be considered at a tree node. Should be >= 0, defaults to 0.

min.rows

Minimum number of instances each child must have after split.

learn.rate

The learning rate or step size, defaults to 0.1.

sample.rate

Fraction of the training data used for learning each decision tree, defaults to 1.0.

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.

thresholds

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

Seed for random numbers.

checkpoint.interval

Set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations, defaults to 10.

cache.node.ids

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.

max.memory

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.

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.

Other Spark ML routines: ml_als_factorization, ml_decision_tree, ml_generalized_linear_regression, ml_kmeans, ml_lda, ml_linear_regression, ml_logistic_regression, ml_multilayer_perceptron, ml_naive_bayes, ml_one_vs_rest, ml_pca, ml_random_forest, ml_survival_regression