`R/rand_forest_randomForest.R`

`details_rand_forest_randomForest.Rd`

`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.

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.