R: Spark ML - Decision Trees

ml_decision_tree_classifier {sparklyr}

R Documentation

Spark ML – Decision Trees

Description

Perform classification and regression using decision trees.

Usage

ml_decision_tree_classifier(x, formula = NULL, max_depth = 5L,
  max_bins = 32L, min_instances_per_node = 1L, min_info_gain = 0,
  impurity = "gini", seed = NULL, thresholds = NULL,
  cache_node_ids = FALSE, checkpoint_interval = 10L,
  max_memory_in_mb = 256L, features_col = "features", label_col = "label",
  prediction_col = "prediction", probability_col = "probability",
  raw_prediction_col = "rawPrediction",
  uid = random_string("decision_tree_classifier_"), ...)

ml_decision_tree(x, formula = NULL, type = c("auto", "regression",
  "classification"), features_col = "features", label_col = "label",
  prediction_col = "prediction", variance_col = NULL,
  probability_col = "probability", raw_prediction_col = "rawPrediction",
  checkpoint_interval = 10L, impurity = "auto", max_bins = 32L,
  max_depth = 5L, min_info_gain = 0, min_instances_per_node = 1L,
  seed = NULL, thresholds = NULL, cache_node_ids = FALSE,
  max_memory_in_mb = 256L, uid = random_string("decision_tree_"),
  response = NULL, features = NULL, ...)

ml_decision_tree_regressor(x, formula = NULL, max_depth = 5L,
  max_bins = 32L, min_instances_per_node = 1L, min_info_gain = 0,
  impurity = "variance", seed = NULL, cache_node_ids = FALSE,
  checkpoint_interval = 10L, max_memory_in_mb = 256L, variance_col = NULL,
  features_col = "features", label_col = "label",
  prediction_col = "prediction",
  uid = random_string("decision_tree_regressor_"), ...)

Arguments

`x`	A `spark_connection`, `ml_pipeline`, or a `tbl_spark`.
`formula`	Used when `x` is a `tbl_spark`. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.
`max_depth`	Maximum depth of the tree (>= 0); that is, the maximum number of nodes separating any leaves from the root of the tree.
`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.
`min_instances_per_node`	Minimum number of instances each child must have after split.
`min_info_gain`	Minimum information gain for a split to be considered at a tree node. Should be >= 0, defaults to 0.
`impurity`	Criterion used for information gain calculation. Supported: "entropy" and "gini" (default) for classification and "variance" (default) for regression. For `ml_decision_tree`, setting `"auto"` will default to the appropriate criterion based on model type.
`seed`	Seed for random numbers.
`thresholds`	Thresholds in multi-class classification to adjust the probability of predicting each class. Array 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.
`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`.
`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.
`max_memory_in_mb`	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.
`features_col`	Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by `ft_r_formula`.
`label_col`	Label column name. The column should be a numeric column. Usually this column is output by `ft_r_formula`.
`prediction_col`	Prediction column name.
`probability_col`	Column name for predicted class conditional probabilities.
`raw_prediction_col`	Raw prediction (a.k.a. confidence) column name.
`uid`	A character string used to uniquely identify the ML estimator.
`...`	Optional arguments; see Details.
`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.
`variance_col`	(Optional) Column name for the biased sample variance of prediction.
`response`	(Deprecated) The name of the response column (as a length-one character vector.)
`features`	(Deprecated) The name of features (terms) to use for the model fit.

Details

When x is a tbl_spark and formula (alternatively, response and features) is specified, the function returns a ml_model object wrapping a ml_pipeline_model which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument predicted_label_col (defaults to "predicted_label") can be used to specify the name of the predicted label column. In addition to the fitted ml_pipeline_model, ml_model objects also contain a ml_pipeline object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by ml_save with type = "pipeline" to faciliate model refresh workflows.

ml_decision_tree is a wrapper around ml_decision_tree_regressor.tbl_spark and ml_decision_tree_classifier.tbl_spark and calls the appropriate method based on model type.

Value

The object returned depends on the class of x.