details_rand_forest_randomForest | R Documentation |
randomForest::randomForest()
fits a model that creates a large number of
decision trees, each independent of the others. The final prediction uses all
predictions from the individual trees and combines them.
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: 500L)
min_n
: Minimal Node Size (type: integer, default: see below)
mtry
depends on the number of columns and the model mode. The default
in randomForest::randomForest()
is
floor(sqrt(ncol(x)))
for classification and floor(ncol(x)/3)
for
regression.
min_n
depends on the mode. For regression, a value of 5 is the
default. For classification, a value of 10 is used.
rand_forest( mtry = integer(1), trees = integer(1), min_n = integer(1) ) %>% set_engine("randomForest") %>% set_mode("regression") %>% translate()
## Random Forest Model Specification (regression) ## ## Main Arguments: ## mtry = integer(1) ## trees = integer(1) ## min_n = integer(1) ## ## Computational engine: randomForest ## ## Model fit template: ## randomForest::randomForest(x = missing_arg(), y = missing_arg(), ## mtry = min_cols(~integer(1), x), ntree = integer(1), nodesize = min_rows(~integer(1), ## x))
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("randomForest") %>% set_mode("classification") %>% translate()
## Random Forest Model Specification (classification) ## ## Main Arguments: ## mtry = integer(1) ## trees = integer(1) ## min_n = integer(1) ## ## Computational engine: randomForest ## ## Model fit template: ## randomForest::randomForest(x = missing_arg(), y = missing_arg(), ## mtry = min_cols(~integer(1), x), ntree = integer(1), nodesize = min_rows(~integer(1), ## x))
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 object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.
The “Fitting and Predicting with parsnip” article contains
examples
for rand_forest()
with the "randomForest"
engine.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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