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_"),
...
)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 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 |
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 length-one 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 length-one character vector.) |
features |
(Deprecated) The name of features (terms) to use for the model fit. |
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.
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.
The object returned depends on the class of x.
spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to
a Spark Predictor object and can be used to compose
Pipeline objects.
ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with
the predictor appended to the pipeline.
tbl_spark: When x is a tbl_spark, a predictor is constructed then
immediately fit with the input tbl_spark, returning a prediction model.
tbl_spark, with formula: specified When formula
is specified, the input tbl_spark is first transformed using a
RFormula transformer before being fit by
the predictor. The object returned in this case is a ml_model which is a
wrapper of a ml_pipeline_model.
See http://spark.apache.org/docs/latest/ml-classification-regression.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()
## Not run: 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) ## End(Not run)
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