For this engine, there are multiple modes: classification and regression
This model has 3 tuning parameters:
mtry
: # Randomly Selected Predictors (type: integer, default: see below)
trees
: # Trees (type: integer, default: 20L)
min_n
: Minimal Node Size (type: integer, default: 1L)
mtry
depends on the number of columns and the model mode. The default in [sparklyr::ml_random_forest()] is floor(sqrt(ncol(x)))
for classification and floor(ncol(x)/3)
for regression.
rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) %>%
set_engine("spark") %>%
set_mode("regression") %>%
translate()
## Random Forest Model Specification (regression)
##
## Main Arguments:
## mtry = integer(1)
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: spark
##
## Model fit template:
## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(),
## type = "regression", feature_subset_strategy = integer(1),
## num_trees = integer(1), min_instances_per_node = min_rows(~integer(1),
## x), seed = sample.int(10^5, 1))
min_rows()
and min_cols()
will adjust the number of neighbors if the chosen value if it is not consistent with the actual data dimensions.
rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) %>%
set_engine("spark") %>%
set_mode("classification") %>%
translate()
## Random Forest Model Specification (classification)
##
## Main Arguments:
## mtry = integer(1)
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: spark
##
## Model fit template:
## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(),
## type = "classification", feature_subset_strategy = integer(1),
## num_trees = integer(1), min_instances_per_node = min_rows(~integer(1),
## 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.
For models created using the "spark"
engine, there are several things to consider.
fit()
is available; using fit_xy()
will generate an error. 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.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.
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