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 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 |
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 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. |
variance_col |
(Optional) Column name for the biased sample variance of prediction. |
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. |
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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