Spark ML - Decision Trees
ml_decision_tree_classifier
Description
Perform classification and regression using decision trees.
Usage
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_"),
...
)Arguments
| Arguments | Description |
|---|---|
| x | A spark_connection, ml_pipeline, or a tbl_spark. |
| formula | Used when x is a tbl_spark. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details. |
| 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 ml_decision_tree, setting "auto" will default to the appropriate criterion based on model type. |
| 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 p/t is predicted, where p is the original probability of that class and t is the class’s threshold. |
| cache_node_ids | If FALSE, the algorithm will pass trees to executors to match instances with nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Defaults to FALSE. |
| 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 ft_r_formula. |
| label_col | Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula. |
| 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. "regression" treats the response as a continuous variable, while "classification" treats the response as a categorical variable. When "auto" is used, the model type is inferred based on the response variable type - if it is a numeric type, then regression is used; classification otherwise. |
| 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. |
Details
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.
Value
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.
See Also
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()
Examples
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)