ml_gradient_boosted_trees {sparklyr} | R Documentation

## Spark ML – Gradient-Boosted Tree

### Description

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`.

### See Also

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`

[Package sparklyr version 0.6.3 Index]