View source: R/ml_model_decision_tree.R
ml_decision_tree_classifier  R Documentation 
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. 
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.
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.
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()
## 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
dt_model < iris_training %>%
ml_decision_tree(Species ~ .)
pred < ml_predict(dt_model, iris_test)
ml_multiclass_classification_evaluator(pred)
## End(Not run)
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