details_boost_tree_spark | R Documentation |
sparklyr::ml_gradient_boosted_trees()
creates a series of decision trees
forming an ensemble. Each tree depends on the results of previous trees.
All trees in the ensemble are combined to produce a final prediction.
For this engine, there are multiple modes: classification and regression. However, multiclass classification is not supported yet.
This model has 7 tuning parameters:
tree_depth
: Tree Depth (type: integer, default: 5L)
trees
: # Trees (type: integer, default: 20L)
learn_rate
: Learning Rate (type: double, default: 0.1)
mtry
: # Randomly Selected Predictors (type: integer, default: see
below)
min_n
: Minimal Node Size (type: integer, default: 1L)
loss_reduction
: Minimum Loss Reduction (type: double, default: 0.0)
sample_size
: # Observations Sampled (type: integer, default: 1.0)
The mtry
parameter is related to the number of predictors. The default
depends on the model mode. For classification, the square root of the
number of predictors is used and for regression, one third of the
predictors are sampled.
boost_tree( mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(), learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric() ) %>% set_engine("spark") %>% set_mode("regression") %>% translate()
## Boosted Tree Model Specification (regression) ## ## Main Arguments: ## mtry = integer() ## trees = integer() ## min_n = integer() ## tree_depth = integer() ## learn_rate = numeric() ## loss_reduction = numeric() ## sample_size = numeric() ## ## Computational engine: spark ## ## Model fit template: ## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(), ## type = "regression", feature_subset_strategy = integer(), ## max_iter = integer(), min_instances_per_node = min_rows(integer(0), ## x), max_depth = integer(), step_size = numeric(), min_info_gain = numeric(), ## subsampling_rate = numeric(), seed = sample.int(10^5, 1))
boost_tree( mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(), learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric() ) %>% set_engine("spark") %>% set_mode("classification") %>% translate()
## Boosted Tree Model Specification (classification) ## ## Main Arguments: ## mtry = integer() ## trees = integer() ## min_n = integer() ## tree_depth = integer() ## learn_rate = numeric() ## loss_reduction = numeric() ## sample_size = numeric() ## ## Computational engine: spark ## ## Model fit template: ## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(), ## type = "classification", feature_subset_strategy = integer(), ## max_iter = integer(), min_instances_per_node = min_rows(integer(0), ## x), max_depth = integer(), step_size = numeric(), min_info_gain = numeric(), ## subsampling_rate = numeric(), 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.
Luraschi, J, K Kuo, and E Ruiz. 2019. Mastering Spark with R. O’Reilly Media
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.