ml_random_forest_classifier: Spark ML -- Random Forest

Description

Perform classification and regression using random forests.

Usage

ml_random_forest_classifier(
  x,
  formula = NULL,
  num_trees = 20,
  subsampling_rate = 1,
  max_depth = 5,
  min_instances_per_node = 1,
  feature_subset_strategy = "auto",
  impurity = "gini",
  min_info_gain = 0,
  max_bins = 32,
  seed = NULL,
  thresholds = NULL,
  checkpoint_interval = 10,
  cache_node_ids = FALSE,
  max_memory_in_mb = 256,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  probability_col = "probability",
  raw_prediction_col = "rawPrediction",
  uid = random_string("random_forest_classifier_"),
  ...
)
ml_random_forest(
  x,
  formula = NULL,
  type = c("auto", "regression", "classification"),
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  probability_col = "probability",
  raw_prediction_col = "rawPrediction",
  feature_subset_strategy = "auto",
  impurity = "auto",
  checkpoint_interval = 10,
  max_bins = 32,
  max_depth = 5,
  num_trees = 20,
  min_info_gain = 0,
  min_instances_per_node = 1,
  subsampling_rate = 1,
  seed = NULL,
  thresholds = NULL,
  cache_node_ids = FALSE,
  max_memory_in_mb = 256,
  uid = random_string("random_forest_"),
  response = NULL,
  features = NULL,
  ...
)
ml_random_forest_regressor(
  x,
  formula = NULL,
  num_trees = 20,
  subsampling_rate = 1,
  max_depth = 5,
  min_instances_per_node = 1,
  feature_subset_strategy = "auto",
  impurity = "variance",
  min_info_gain = 0,
  max_bins = 32,
  seed = NULL,
  checkpoint_interval = 10,
  cache_node_ids = FALSE,
  max_memory_in_mb = 256,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  uid = random_string("random_forest_regressor_"),
  ...
)

Value

The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.

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.
num_trees: Number of trees to train (>= 1). If 1, then no bootstrapping is used. If > 1, then bootstrapping is done.
subsampling_rate: Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)
max_depth: Maximum depth of the tree (>= 0); that is, the maximum number of nodes separating any leaves from the root of the tree.
min_instances_per_node: Minimum number of instances each child must have after split.
feature_subset_strategy: The number of features to consider for splits at each tree node. See details for options.
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.
min_info_gain: Minimum information gain for a split to be considered at a tree node. Should be >= 0, defaults to 0.
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.
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.
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_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.
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

The supported options for feature_subset_strategy are

"auto": Choose automatically for task: If num_trees == 1, set to "all". If num_trees > 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
"all": use all features
"onethird": use 1/3 of the features
"sqrt": use use sqrt(number of features)
"log2": use log2(number of features)
"n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

ml_random_forest is a wrapper around ml_random_forest_regressor.tbl_spark and ml_random_forest_classifier.tbl_spark and calls the appropriate method based on model type.

Examples

Run this code

if (FALSE) {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

partitions <- iris_tbl %>%
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

iris_training <- partitions$training
iris_test <- partitions$test

rf_model <- iris_training %>%
  ml_random_forest(Species ~ ., type = "classification")

pred <- ml_predict(rf_model, iris_test)

ml_multiclass_classification_evaluator(pred)
}