details_decision_tree_spark | R Documentation |
sparklyr::ml_decision_tree()
fits a model as a set of if/then
statements that creates a tree-based structure.
For this engine, there are multiple modes: classification and regression
This model has 2 tuning parameters:
tree_depth
: Tree Depth (type: integer, default: 5L)
min_n
: Minimal Node Size (type: integer, default: 1L)
decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% set_engine("spark") %>% set_mode("classification") %>% translate()
## Decision Tree Model Specification (classification) ## ## Main Arguments: ## tree_depth = integer(1) ## min_n = integer(1) ## ## Computational engine: spark ## ## Model fit template: ## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(), ## max_depth = integer(1), min_instances_per_node = min_rows(0L, ## x), seed = sample.int(10^5, 1))
decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% set_engine("spark") %>% set_mode("regression") %>% translate()
## Decision Tree Model Specification (regression) ## ## Main Arguments: ## tree_depth = integer(1) ## min_n = integer(1) ## ## Computational engine: spark ## ## Model fit template: ## sparklyr::ml_decision_tree_regressor(x = missing_arg(), formula = missing_arg(), ## max_depth = integer(1), min_instances_per_node = min_rows(0L, ## x), seed = sample.int(10^5, 1))
This engine does not require any special encoding of the predictors.
Categorical predictors can be partitioned into groups of factor levels
(e.g. {a, c}
vs {b, d}
) when splitting at a node. Dummy variables
are not required for this model.
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on tidymodels.org
.
The fit()
and fit_xy()
arguments have arguments called
case_weights
that expect vectors of case weights.
Note that, for spark engines, the case_weight
argument value should be
a character string to specify the column with the numeric case weights.
For models created using the "spark"
engine, there are several things
to consider.
Only the formula interface to via fit()
is available; using
fit_xy()
will generate an error.
The predictions will always be in a Spark table format. The names will be the same as documented but without the dots.
There is no equivalent to factor columns in Spark tables so class predictions are returned as character columns.
To retain the model object for a new R session (via save()
), the
model$fit
element of the parsnip object should be serialized via
ml_save(object$fit)
and separately saved to disk. In a new session,
the object can be reloaded and reattached to the parsnip object.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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