R/ml_classification_decision_tree_classifier.R
, R/ml_model_decision_tree.R
, R/ml_regression_decision_tree_regressor.R
ml_decision_tree.Rd
Perform classification and regression using decision trees.
ml_decision_tree_classifier( x, formula = NULL, max_depth = 5, max_bins = 32, min_instances_per_node = 1, min_info_gain = 0, impurity = "gini", seed = NULL, thresholds = NULL, cache_node_ids = FALSE, checkpoint_interval = 10, max_memory_in_mb = 256, 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 = 5, max_bins = 32, min_instances_per_node = 1, min_info_gain = 0, impurity = "variance", seed = NULL, cache_node_ids = FALSE, checkpoint_interval = 10, max_memory_in_mb = 256, variance_col = NULL, features_col = "features", label_col = "label", prediction_col = "prediction", uid = random_string("decision_tree_regressor_"), ... )
x  A 

formula  Used when 
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

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 
cache_node_ids  If 
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 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. 
variance_col  (Optional) Column name for the biased sample variance of prediction. 
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. 
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
.
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.
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.
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_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()
,
ml_random_forest_classifier()
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 dt_model < iris_training %>% ml_decision_tree(Species ~ .) pred < ml_predict(dt_model, iris_test) ml_multiclass_classification_evaluator(pred) }