Spark ML  Random Forest
Perform classification and regression using random forests.
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_"), ...)
Arguments
x  A 
formula  Used when 
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

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 multiclass 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 
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 
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 lengthone character vector. The column should be single vector column of numeric values. Usually this column is output by 
label_col  Label column name. The column should be a numeric column. Usually this column is output by 
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. 
response  (Deprecated) The name of the response column (as a lengthone character vector.) 
features  (Deprecated) The name of features (terms) to use for the model fit. 
Value
The object returned depends on the class of x
.
spark_connection
: Whenx
is aspark_connection
, the function returns an instance of aml_estimator
object. The object contains a pointer to a SparkPredictor
object and can be used to composePipeline
objects.ml_pipeline
: Whenx
is aml_pipeline
, the function returns aml_pipeline
with the predictor appended to the pipeline.tbl_spark
: Whenx
is atbl_spark
, a predictor is constructed then immediately fit with the inputtbl_spark
, returning a prediction model.tbl_spark
, withformula
: specified Whenformula
is specified, the inputtbl_spark
is first transformed using aRFormula
transformer before being fit by the predictor. The object returned in this case is aml_model
which is a wrapper of aml_pipeline_model
.
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 preprocessing transformers, the ML predictor, and, for classification models, a postprocessing 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.
The supported options for feature_subset_strategy
are
"auto"
: Choose automatically for task: Ifnum_trees == 1
, set to"all"
. Ifnum_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"
: whenn
is in the range (0, 1.0], use n * number of features. Whenn
is in the range (1, number of features), usen
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.
See also
See http://spark.apache.org/docs/latest/mlclassificationregression.html for more information on the set of supervised learning algorithms.
Other ml algorithms: ml_aft_survival_regression
,
ml_decision_tree_classifier
,
ml_gbt_classifier
,
ml_generalized_linear_regression
,
ml_isotonic_regression
,
ml_linear_regression
,
ml_linear_svc
,
ml_logistic_regression
,
ml_multilayer_perceptron_classifier
,
ml_naive_bayes
,
ml_one_vs_rest
Examples
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)
}